Training data quality is an evaluation of a data set’s fitness to serve its purpose in a given ML use case. Your requirements will be driven by the use case, and you will need to evaluate the quality of your data annotation over multiple dimensions, including completeness, exactness, and accuracy.

The process of annotating data always includes some human decisions. The first challenge is actually to have humans agree on what is a correct annotation of the recorded data, and creating such annotation guidelines is sometimes not as easy as one might think. We are experienced in how to efficiently design annotation guidelines that enhance the quality, and we will share some of our insights in a later blog post.

Why is data quality important?

 For example, if you train a computer vision system for autonomous vehicles with images of mislabelled road lane lines, the results could be disastrous. In order to develop accurate algorithms, you will need high-quality training data labelled by skilled annotators. To conclude, high-quality training data is necessary for a successful AI initiative. Before you begin to launch your AI initiative, pay attention to your data quality and develop data quality assurance practices to realize the best return on your investment.

Defining Quality of Training Data

Data quality is an assessment whether the given data is fit for purpose. Not every kind of data, and not every data source, is useful or of sufficiently high quality for the machine learning algorithms that power artificial intelligence development – no matter the ultimate purpose of that AI application.

To be more specific, the quality of data is determined by accuracy, consistency, completeness, timeliness and integrity.

• Accuracy: It measures how reliable a dataset is by comparing it against a known, trustworthy reference data set.
• Consistency: Data is consistent when the same data located in different storage areas can be considered equivalent.
• Completeness: the data should not have missing values or miss data records.
• Timeliness: the data should be up to date.
• Integrity: High-integrity data conforms to the syntax (format, type, range) of its definition provided by e.g. a data model

Standard Quality Assurance Methods

Here are some of the more common data quality measurement processes:

1. Benchmarks or gold set Method

It helps measure how well a set of annotations from a group or individual matches the vetted benchmark established by knowledge experts or data scientists. Benchmarks tend to be the most affordable QA option since it involves the least amount of overlapping work. Benchmarks can provide a useful reference point as you continue to measure your output’s quality during the project. They can also be used as test datasets to screen annotation candidates.

2. Consensus Method

Consensus measures the percentage of agreement between multiple human or machine annotators. To calculate a consensus score, it is necessary to divide the sum of agreeing labels by the total number of labels per asset. The goal is to arrive at a consensus decision for each item. An auditor typically arbitrates any disagreement amongst the overlapped judgments. Consensus can be either performed by assigning a certain number of reviewers per data point or be automated.

3. Cronbach’s alpha test

This test  is an algorithm used to measure the average correlation or consistency of items in a dataset. Depending on the characteristics of research (for instance, its homogeneity), it may help quickly assess the labels’ overall reliability.

4. Review or Auditing

Auditing is another method to measure data quality. This method is based on the review of label accuracy by a domain expert. The review is usually conducted by visually checking a limited number of labels, but some projects review all labels. TagX enables companies to easily review quality through a sampling portal: a dedicated portal providing full transparency and accountability on data quality. Your team can get full transparency on the batch’s quality and provide direct feedback to data trainers.

Due to the iterative machine learning model testing and validation stages, we must keep in mind that data quality can change during a project. As you train your model or after making your solution live, you’ll probably find patterns in your inaccuracies or identify edge cases that will force you to adapt your dataset. The auditing method of checking the quality of training data measures the accuracy by having review the labels by experts either by checking on the spot or by reviewing all. This method is crucial for projects where auditors review and retread the content until and unless it reaches the highest level of accuracy.

Conclusion

Creating training data is often one of the most expensive components of building a machine learning application. Properly monitoring training data quality increases the chance of having a performant model the first time around. And, getting labels right the first time (first pass quality) is far cheaper than the cost of discovering and redoing work to fix the problem. With world class tooling at your fingertips, you can ensure your labeling maintains the level of quality you need to get the modeling results you want.

With Quality Assurance processes data scientists can:

• Monitor overall consistency and accuracy of training data
• Quickly troubleshoot quality errors
• Improve labeler instructions, on-boarding, and training
• Better understand the specifics to their project on what and how to label

We at TagX,  assure to maintain Quality Standards according to the requirement of the project. We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to table competencies like commitment, confidentiality, flexibility and ownership to each project or collaboration. So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.

 

To compensate for one negative customer experience and rebuild the customer-organization relationship, it takes ten positive ones. According to Gartner, 85 percent of all customer contacts will be handled without the need of humans. Self-service technology like AI chatbots, device guides, decision trees, and others free up call centre workers to focus on more difficult tasks rather than answering repetitive client enquiries. Automation is present in every business, including the customer service industry.

Artificial Intelligence for Contact Centers

Advancements in digital technology continue to transform customer service interactions across industries. With AI today, complex customer queries are no longer a burden. Build a virtual assistant that allows your customers to easily search databases to find the answers they are looking for, eliminating the need to dig through standard operating procedures.

Furthermore, answering client queries more quickly and accurately allows agents to work smarter while lowering support costs. It allows you to directly resolve your customers’ questions on the front end, while also providing your agents with the information and resources they require on the back end.

End-to-end customer service with AI, enabling improvements in loyalty and brand reputation to new revenue streams. Real-time self-service and AI assistance in the customer service industry bring huge opportunities to forward-thinking businesses.

Automation can deliver a level of responsiveness that businesses could not even think otherwise. In the future, virtual assistants will be able to predict what your customers are looking for by analyzing how users are interacting with your brand. If your intelligent virtual assistant has an idea of who your customers really are, it will be able to deliver answers way before customers even know that they have concerns.

Advantages of Customer Service Automation

1. Faster response time 

In an average service call, if you notice 75% of that time, agents are doing manual research while actual human customer interaction is limited to only 25%. This delay can be avoided entirely if AI could be used to go through company servers, previous occurrences of the related problem, and come up with a detailed guide or step-by-step solution.

Without a doubt, the quicker a problem is solved, the happier your customers will be. In fact, according to a study, 39% of customers try to solve a problem themselves by visiting the FAQ page first, before seeking the help of customer service. The customer service desk can speed up service multifold by developing a virtual assistant to handle customer queries on the front end.

2. Proactive action

If the customer service desk can solve a problem before it even arises, it makes for a really happy and satisfied customer. AI has huge potential to analyze customer data, past interactions with agents, and identify frequent customer issues and problems by monitoring websites, and in-app activity for distress indicators.

3. Reliable, 24*7 customer service

Enabling automated customer service has the capabilities to respond to your customer’s queries within minutes. Customers don’t want to wait around for several hours or days for a response.

Connecting with your customers on a personal level even while using automation is the key factor to growth. A study showed that 42% of customers become repeat customers after a good customer service experience, whilst 52% stopped their purchases after a single poor customer service interaction.

4. Leaving no room for error

Automation removes or we can say minimizes the human element in this area of your customer service, which boosts the potential for idleness, futile effort, and human error.

5. Lesser cost

On the business end, on average, it takes $5000 to train a customer service agent, not to mention the monthly salary which you have to provide. On the flip side, AI needs to be trained once and it can be trusted to remember every one of its lessons.

6. Prioritizing queries

The simplest and most common queries can be handled by automated responses, FAQs, and step-by-step text as well as visual guides. However, for more serious and complicated support requests, you can implement a knowledge base software that enables self-service across all touchpoints.

7. Reduction in duplicate efforts

When a customer request is transferred to a new employee, the agent should get the records of the conversation that the customer was already having, along with probable solutions and guides. Customers don’t like having to repeat their problems to a second or third person. According to a report by Capterra, 72% of customers blame a poor customer experience on having to explain their problem multiple times.

Because most companies have multiple communication channels, including instant chat on website and app, email, query submission pages, and phone automation, it’s mandatory to have a unified platform where all the data regarding a single query gets compiled, regardless of the channel it came in through. This reduces duplication of effort for both the customer as well as a service agent.

Wrapping Up

An intelligent automation platform can redefine end-to-end customer interactions effectively too at a lower cost. This enables companies to create greater efficiencies, lower down customer handling time, and fully automate their business operations end-to-end. Thus, the way forward, a key consideration should be implementing a solution that integrates business processes with artificial intelligence.

Insurance firms frequently outsource insurance data entry services to alleviate their obstacles, headaches, and big issues. Their large volume of papers must be processed in a timely, correct, secure, and cost-effective manner, which is why they rely on specialised services that provide all of these advantages.

These services have the best technology, infrastructure, and professional employees to deal with large amounts of data on a daily basis. It assists insurance clients in increasing their overall business efficiency and focusing on their core competencies, as well as making informed decisions. This was made possible by the streamlined support provided by third-party companies.

Hiring such offshore enterprises ensures that insurance clients obtain the appropriate core expertise, saving them time, effort, and operating costs. The outcomes of these outsourcing services are exact and high-quality, with the best security measures in place to ensure sensitive data is protected.

The reasons why insurance companies outsource insurance data entry services are as follows:

Accurate Results

The proficient data entry services staff in offshore companies utilize optimum workflows that always ensure clients receive highly accurate outcomes. The reliable scrutiny procedures are done with progressive technologies in multi-tier setups and with the best manual supervision. 

Rapid Turnaround Times 

The third-party or offshore companies provide round-the-clock support to all clients with their personnel working in different shifts. Even if the projects are complicated or enormous, they will ensure to add additional resources to get it done efficiently and on time. They have rapid turnaround times for all their processes gained through their years of experience and industry exposure.

Capacity for Massive Processing

The massive, qualified personnel strength and best-of-breed technology of offshore firms ensure insurance companies receive their voluminous projects at their stipulated time frames. There won’t be any compromise in the data quality and accuracy levels even when the turnaround time is rapid. 

Expertise at a Low Cost

Client companies can save their investments in infrastructure, staffing, and technology costs with outsourcing as third-party companies already have all these in place. These services are reasonably-priced even though they offer results in the highest-quality and accuracy levels. As a result, insurance organizations can save on their operational expenses by great figures which can be pooled into other core areas.

Data security and confidentiality

The state-of-the-art technologies deployed offshore are lined with the stringent security features that ensure total protection and confidentiality to sensitive information from all kinds of cyber threats, breaches, or data leaks. They also have stringent backup mechanisms to protect this information from all kinds of loss or disasters. Even complete confidentiality contracts such as non-disclosure agreements (NDAs) are signed with clients before project commencement.

These are the highly favorable reasons as to why insurance companies prefer outsource insurance data entry services rather than doing it within their organization. It eliminates all their hassles with processing delays, inaccurate and inconsistent data, security concerns, and more. Availability of these solutions will exponentially boost their productivity and efficiency levels in the long run. The added benefit of cost-effectiveness will help them gain substantial profits, better ROIs, and stable revenues.

Why Choose TagX

At TagX, we provide quality insurance data entry services and a series of other insurance services to global clients. Our cost-effective services help clients to save money and time, which can be invested in improving other core competencies. Whether you need insurance claims data entry services or data mining services, our dedicated team can assist you with all your business’ basic and data entry requirements. Outsource insurance data analytics services and data entry services for insurance to us and take advantage of all benefits we offer.

Can you imagine an industry that involves more challenge than agriculture? You reap what you sow, they say. But what they forget to add is “if you’re lucky.” When the weather strikes or crops get affected by disease, farmers can hardly talk about yields. Or when a global pandemic hits, all of a sudden it gets harder to manage various processes because most are not digital.

At the same time, the global population is growing, and urbanization is continuing. Disposable income is rising, and consumption habits are changing. Farmers are under a lot of pressure to meet the increasing demand, and they need a way to increase productivity. Thirty years from now, there will be more people to feed. And since the amount of fertile soil is limited, there will also be a need to move beyond traditional farming.

We need to look for ways to help farmers minimize their risks, or at least make them more manageable. Implementing artificial intelligence in agriculture on a global scale is one of the most promising opportunities.

Artificial Intelligence is being used by the agriculture industry to help produce healthier crops, control pests, monitor soil and growing conditions, organise data for farmers, reduce effort, and improve a wide range of agriculture-related operations along the food supply chain.

Image Annotation for Agriculture

In agriculture field image annotation helps to make crops and other things recognizable to make the right decision without use of humans. So, let’s find out what image annotation can do for an agricultural field and how it is utilized in machine learning and AI.

Crops and Vegetables Detection

The robots used agriculture and farming to detect the crops including fruits and vegetables for performing various tasks. Image annotation annotates the crops to make them recognizable to machine learning models like robots or drones.

Annotation to Check Plants Fructification

Just like detecting the crops, image annotation also helps to check the fructification level of plants, if they are ready for harvesting or what is the maturity level of plants. Image annotation techniques can help to detect such plants and inform the farmers to take actions as per the crops Fructification levels.

Crops Health Monitoring

Apart from detecting the crops, image annotation also helps computer vision models to check or monitor the health of the crops through deep learning AI model training. The robots can closely monitor the crop or plant and analyze its condition whether its matured, not matured, infected or need pesticides to protect from insects and other harmful pests.

Live Stock Management

Animal husbandry, that usually comes in farming, you can say part of the agricultural sector can be also managed by AI-enabled devices. Image annotation helps to detect and recognize the animals helping the farmers to keep an eye and monitor the live stocks making the animal husbandry business profitable. Bounding box annotation and polygon annotation helps to recognize the animals precisely.

Monitoring soil health

AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients.The type of soil and nutrition of soil plays an important factor in the type of crop is grown and the quality of the crop. It’s time to implement  image recognition-based technology. that can identify the nutrient deficiencies in soil including plant pests and diseases by which farmers can also get an idea to use fertilizer which helps to improve harvest quality. The farmer can capture images of plants using smartphones. TagX can label these images for model training.

Drone Imagery Data Annotation

In the agricultural sector image annotation is also used for geo-sensing to check the soil condition and other attributes of the agricultural fields to analyze the situation to decide for the right timing of crop sowing and harvesting. Basically drones are used and semantic segmentation image annotation technique is used to observe and monitor the health condition of the various agricultural fields.

Unwanted Crops Detection

With useful crops there are many unwanted crops that utilize the minerals of the soil under the roots, that should reach the main crop. Such unwanted plants are called weeds that should be removed to improve the crop yield of the crops and boost the productivity of the entire agricultural field.

Conclusion

In the future, AI will help farmers evolve into agricultural technologists, using data to optimize yields down to individual rows of plants. Artificial Intelligence in agriculture not only helps farmers to automate their farming but also shifts to precise cultivation for higher crop yield and better quality while using fewer resources.

Companies involved in improving machine learning or Artificial Intelligence-based products or services like training data for agriculture, drone, and automated machine making will get technological advancement in the future and will provide more useful applications to this sector helping the world deal with food production issues for the growing population.

TagX provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our  managed service team is ready to support you in both  deploying and maintaining your AI and ML projects.

TagX is really pleased to be part of  “Best Natural Language Processing Startups in India of 2021” published  by Startup pill. This article showcases Startup Pill’s top picks for Natural Language Processing Startups. We started our journey a year back and are striving hard to make our presence in the AI world. TagX is growing each day learning and providing high quality services to our clients.

TagX is providing Data Annotation for Computer Vision and Natural Language processing applications. In practice, data annotation is the process of transcribing, tagging, and labeling significant features within your data. These are the features that you want your machine learning system to recognize on its own, with real-world data that hasn’t been annotated.

To maintain the Quality Standards expected by our clients we follow agile processes and protocols.

Natural Language Processing

Natural language is the spoken words that you use in daily conversations with other people. Not long ago, machines could not understand it. But now, data scientists are working on artificial intelligence technology that can understand natural language, unlocking future breakthroughs and immense potential. TagX offers natural language annotation services for machine learning with an optimum level of accuracy. NLP annotation helps machine learning to acquire only the useful words from the sentence and make it understandable for AI words.

In addition, there are three main actions performed by natural language processing technology: understand, action, and reaction.

1. Understand: First, the machine must understand the meaning of what the user is saying in words. This step uses natural language understanding (NLU), a subset of NLP.
2. Action: Second, the machine must react to what the user said. For example, if you said “Hey Alexa, order toilet paper on Amazon!” Alexa will understand and do that for you.
3. Reaction: Finally, the machine must react to what the user said. Once Alexa has successfully ordered headphones for you on Amazon, she should tell you: “I ordered headphones and it should be delivered tomorrow.”

TagX aims to provide a One-Stop Solution for AI firms to overcome this gap by utilising a natural language processing annotation solution for a variety of uses including speech recognition, sentiment analysis, virtual support, and chatbots.

Various types of annotation that can be performed on Text or Audio are:

• Named entity recognition
• Part of speech tagging
• Keyphrase Tagging
• Sentiment Analysis
• Text or Audio Classification
• Text or Audio transcription
• Summarization

With the perfect blend of experience and skills, our outsourced data annotation services consistently deliver structured, highest-quality, and large volumes of data streams within the desired time and budget. As one of the leading providers of data labeling services, we have worked with clients across different industry verticals such as Satellite Imagery, Insurance, Logistics, Retail, and more.

If you want to train your NLP model , reach out to our Team TagX today. Read the full article at Startup pill Article.

 

Teams must decide what type of data annotation is right for their application. This is an important question because data labeling can be expensive and time-consuming, but it is critical to the model’s success. So, teams are stuck with an often complicated cost-benefit analysis when it comes time to annotate their data. While it might be tempting to settle for image classification – it’s probably the cheapest and easiest to achieve – its applications are very limited. If we think about an autonomous vehicle computer vision model looking out onto a complex urban environment, we begin to see that just recognizing whether there is a human in its sight or not will not be enough. To avoid running the person over, the car also needs to know where the human is. If we take a medical computer vision application – identifying the shape of cancerous cells, we need instance segmentation, to differentiate between different instances of cells. Defining the whole image as “cells” won’t help us localize the problematic cells or to understand the extent of any problems.

But there are many cases where it’s not obvious what type of data annotation you need. This is a high-risk decision for teams. If they use the wrong annotation method or add the wrong information to their images, their model may not work and they’ll need to start the data labeling process anew. Simulated Data can relieve a lot of the stress associated with this type of decision by automatically and flexibly adding a wider range of annotations with perfect ground truth, but more on this later. 

Different Annotation Techniques

Once you’ve chosen your annotation method, there are even more choices to make; now you have to select an annotation technique. This is the actual method that annotators will use to attach annotations to your data. For instance, they may draw squares around objects, do multi-sided polygons, or attach landmarks. It is important to understand these techniques because, again, there is often a tradeoff between cost, time, and effectiveness.  

• Bounding Boxes – The most basic type of data annotation. It consists of drawing a rectangle or square around the target object and is very commonly used, due to its simplicity and versatility. This is useful when objects are relatively symmetrical – such as boxes of foods or road signs – or when the exact shape of the object is of less interest. On the other hand, complex objects don’t have right angles, and achieving ground truth annotation using bounding boxes is impossible. Additionally, we have no annotations for what’s happening “inside” the box. For instance, if we care about a person’s movement, posture, gait, or other dynamic indicators, bounding boxes are unlikely to be helpful.

• Polygon Annotation – a variation of the bounding box technique. By using complex shapes (polygons) and not only the right angles of bounding boxes, the target object’s location, and boundaries are defined more accurately. Increased accuracy cuts out irrelevant pixels that can confuse the classifier. This is good for more irregular shaped objects – cars, people, and logos, animals. While polygons are more accurate than bounding boxes, overlapping objects may be captured within a single polygon and therefore not distinguishable from each other.

• Polylines – this plots continuous lines made of one or more segments. It is best used when important features have a linear appearance. This is common in an autonomous vehicle context, as it is easily applied to define lanes and sidewalks. But, for most use cases this simply isn’t relevant because the object is not linear and more than a single pixel wide. 

• Landmarking – This is also known as dot annotation. It involves creating dots across the image. These small dots help detect and quantify characteristics in the data. It is used often in facial recognition to detect facial features, emotions, and expressions. It can be used to help annotate human bodies, align posture, and explore the relationship between different body parts. Another interesting use case is to find objects of interest in aerial footage such as cars, buildings, and more. Clearly, this approach is both time consuming and prone to inaccuracy. For instance, manually landmarking facial features like irises across thousands of images is very difficult to do consistently and accurately.

• Tracking – This is a data labeling technique used to plot an object’s movement across multiple frames. Some tools include interpolation, which enables the annotator to label one frame, skip frames, and then annotate the new position. The annotating tools automatically fill in the movement and track the objects through the frames. While this is great theoretically, it takes a lot of work and high levels of accuracy to successfully annotate. In general, the cost of annotating video data quickly becomes cost prohibitive because of the need to annotate frame-by-frame.

Often, your use case will dictate the technique that’s right for you. But, even if you have little choice in which technique to adopt, it is critical to be aware of the constraints of each one. Expensive techniques may limit the amount of data you want to collect. While techniques with inherent variation may force you to pay extra attention to the effects of minor inconsistencies on your model’s performance.

Getting the Annotation Done

Now, you’ve gathered your data and decided on the method and techniques of data annotation that work best for your model. It’s time to get the annotations added to your images. This annotating process involves people sitting, and manually marking image after image. Ideally, you might be assisted by some automation tools, but in general, it is a manual and labor-intensive process. In today’s annotation landscape, there are a couple of different solutions available to you:

• Crowdsourcing – Crowdsourcing involves paying workers often distributed globally and working as freelancers to perform a micro-task or assignment. They are generally paid a small sum based on the volume of work they complete. Crowdsourced labor tends to be of low quality and consistency for obvious reasons. The workers are lightly vetted or may have little idea of what they are doing or common pitfalls. The burden of managing them falls on you. There are also platforms that crowdsource work but manage the workflow and sourcing of workers.   Additionally, quality assurance post labeling requires resources and validation and without it is impossible to guarantee high-quality results. Because these tend to be one-off relationships, there is no feedback loop with the people working on your project and there is no way to train them over time. Data security is also a challenge as these people are often working independently on unsecured computers.

• In-House Solutions – Some companies choose to try and solve data annotation needs in-house. For small, easy-to-annotate datasets, this may be a great option. But, many companies often assign this low-level work to their data scientists and engineers, which is not a good use of their time. The alternative of hiring annotators in-house – which brings benefits of process control and QA carries significant overhead costs. Generally, this method is not scalable, as you invest in hiring, managing, and training employees while your data needs may fluctuate wildly over time. Teams that try to automate these processes or build in-house tech solutions often find themselves distracting valuable development teams with projects that would be more efficient to outsource.

• Outsourcing – There are many data labeling companies often based in low-cost markets like India – that employee teams focused on data annotation. Some suppliers leverage certain ML models to accelerate the process and do QA. By virtue of employing the annotators, these companies are better able to control quality, can improve quality over time as they learn about your specific needs, and can provide better time estimates than the other options. But, ultimately this is still a manual process and any cost-savings come from the cheap cost of labor. You still have to devote operational resources to managing this relationship and, at the end of the day, you are still dependent on a third-party vendor that is subject to all kinds of delays, inconsistencies, and challenges.

As you can see, all of these options have significant operational, quality control, and process challenges. They generally force you to devote time and energy to things outside your core technological mandate. 

TagX Data Annotation Services

Since data annotation is very important for the overall success of your AI projects, you should carefully choose your service provider. TagX offers data annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions.

We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to table competencies like commitment, confidentiality, flexibility and ownership to each project or collaboration. So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.

TagX is the best place to start if you require high-quality speech data for a voice recognition solution. We capture speech data in every language, dialect, or non-native accent from any country. To get started, learn more about our data solutions or tell us about your project. You’ll need a lot of training and testing data if you’re constructing a voice recognition system or conversational AI. Where, on the other hand, can you find high-quality voice recognition data? And where do you go for voice recordings with the exact training requirements you require? The good news is that you have choices.

There are hundreds of public speech datasets available online if all you need is a generic dataset. However, if you’re like most voice developers and require speech data tailored to your solution’s specific use cases, you’ll have to collect it yourself. Here’s how to get speech data for your machine-learning algorithms, as well as the advantages and disadvantages of each method.

1. Your Customer Speech Data

The most natural place to start is your own proprietary speech data. If your company has the legal right and sufficient user consent to collect and use your own customer data, then you may already have a speech data training set at your fingertips.

Pros

While there’s an upfront investment to obtaining and processing the data, you won’t have to take on any additional collection costs. If the data is coming from customers using your application, it’s likely already tailored to your solution’s use cases.

Cons

Limitations of your existing product, customer base, or collection methodology may exclude certain target languages or demographics—or may be biased towards one demographic. Most in-house-collected speech data still requires processing, like transcription, tagging, or bucketing, which must be outsourced to a data vendor which results in additional processing costs.

2. Public Speech Datasets 

There are hundreds of publicly available speech recognition datasets that can serve as a great starting point. These datasets are gathered as part of public, open-source research projects with the goal of fostering innovation in the speech technology community. This category also includes data scraped from publicly available sources (like YouTube, for example).

Some popular public speech datasets include:

• The Google Speech Commands Dataset
• Mozilla’s Common Voice Dataset
• The Speech Accent Archive

Pros

This is great news if you don’t have a budget for data collection. These datasets are all available for immediate download. There are hundreds of datasets available, both unscripted or scripted, so if you’re purely after a quantity of speech samples, this may be the best solution for you.

Cons

The majority of these datasets require significant pre-processing and quality assurance before they can be fed into a machine learning algorithm. These speech samples are generic, so while they may be helpful for building a generic speech recognition system, they won’t help you train and test on your product’s specific use cases. As many of these databases are collected through open-source user submissions, they vary widely in audio quality.

3. Pre-Packaged Speech Datasets

If you don’t have your own data and a public dataset doesn’t suit your needs, that’s when you’ll have to explore purchasing data or collecting your own. Pre-packaged datasets are speech datasets that have already been collected by a data vendor for the purpose of resale to multiple clients. Their main benefit is that they are available for immediate download.

These datasets can be quite general—like a pronunciation database, where native speakers of a language read a large number of words. But they can also be created for very specific applications.

Pros

You may be fortunate enough that there’s already been a collection for your specific use case, or for the languages or demographics you’re targeting.In that case, pre-collected datasets can occasionally be more affordable than collecting new data. These datasets can typically be delivered in a matter of days.

Cons

Because the data is pre-packaged, you won’t be able to customize the dataset to your needs. This could mean limited languages, dialects, demographics, audio specifications, or transcription options. You’re confined to the data that was already collected. This data can also be purchased by any other company, meaning it’s not unique to your application.

4. Custom Remote-Collected or Crowd-Sourced Datasets

If you’re building a voice application, it’s unlikely you’ll find an existing dataset that covers all of your training use cases. For example, if you’re building a banking voice recognition app, you’ll need speech samples relating to bank withdrawals, statement balances, and deposits. It’s unlikely any pre-made dataset will cover those cases. That’s when you’ll have to collect your own data, or collect data through a data solutions provider. For example, at TagX, we specialize in collecting speech data for any application in a variety of languages, dialects, and accents.

When it comes to collecting speech data, you have two options: remote collection or in-person collection. Remote-collected speech data is collected through mobile apps or web browser platforms from a trusted crowd. Participants are recruited online based on their language and demographic profile. They’re then asked to record speech samples by reading prompts off their screen or by speaking through a variety of scenarios. For most data collection projects, remote collection is the best option, as it is affordable, scalable, and highly customizable to your needs.

Pros

You can structure the collection to your exact training data specifications.Remote collection is more affordable than in-person collectionYou can collect different types of speech data, including command-based, scenario-based, or unscripted speech. Should you need to collect additional data, the infrastructure is in place to quickly and affordably collect more. As part of the collection project, you can specify your exact transcription and labeling requirements before the data is delivered to you. Because you’ve collected this data yourself, the data won’t be accessible by any of your competitors.

Cons

Because data is collected remotely from participants’ cell phones or headsets, you have fewer choices when it comes to audio or microphone specifications. If you require a particular acoustic scenario, like certain types of background noise, you may need to opt for in-person collection.

5. In-Person or Field-Collected Speech Datasets

In-person collection is typically a larger investment than collecting data remotely. That said, in-person data collection is the best collection option for clients who have specific audio or equipment requirements that otherwise can’t be achieved remotely. For example, you may want to collect voice recordings from the actual microphone used in your speech recognition device. In that case, you would send your device to us at TagX, and we would record participants in person.

Pros

In-person data collection is the most customizable option, as you can control every factor of the collection. In-person collection allows you to record with any hardware device, microphone, or camera. As a result, you can achieve any audio specifications needed for your training and testing data.  As with a remote collection project, the data can be delivered to you fully transcribed and labeled. Again, collecting your own data means you have full proprietary ownership.

Cons

In-field collection is the most expensive collection method, as it can involve travel and building or shipping specialized recording equipment. More sophisticated in-person collections take longer to deliver than remote-collected or pre-packaged data. In-field collection doesn’t offer the participant recruitment convenience of remote collection.

TagX -Your Trusted Partner for Data

TagX is the best place to start if you require high-quality speech data for a voice recognition solution. We capture speech data in every language, dialect, or non-native accent from any country. To get started, learn more about our data solutions or tell us about your project.

We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to table competencies like commitment, confidentiality, flexibility and ownership to each project or collaboration. So, regardless of the type of data you intend to get, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.

You may be involved in a business, whether small or large it would have many tasks to be completed on a daily or weekly basis. Of these tasks, several may be repetitive and can easily be outsourced to another firm so that you can focus on the core areas of your business. The question regarding the cost may arise in your minds, and it is a legitimate one too. However, the truth of the matter is that you will save on costs and time, rather than end up spending, in the process of outsourcing your business to a third party.

Over the last ten years, outsourcing has increased by leaps and bounds, with over three million jobs outsourced. Outsourcing is no longer considered the domain of huge corporations; because of advances in technology, even medium and small enterprises can now outsource their work and reap significant financial rewards. Today, business process outsourcing enables professionals like software engineers, graphic designers, transcriptionists, accounting professionals and other IT specialists to work from any corner of the world. However, outsourcing is not as easy as it seems or sounds. There are several things to be taken into account, the most important being choosing the correct outsourcing partner. Care needs to be taken in selecting an efficient partner with a professional outlook.

The perfect time to outsource

To put it very shortly – there is no better time like now. However, the ‘right time’ to outsource can vary from business to business. While you may have your regular staff to handle day-to-day activities, they may not be able to manage the show if you are planning to expand or take up a new project. When the current workforce is unable to cope with the work pressure, it is time to think of outsourcing to a professional company that has the wherewithal to deliver. Small businesses can explore the possibilities of outsourcing right at the outset itself and take advantage of the cost savings.

What to outsource?

Today, almost every task one can think of can be outsourced. Right from accounting/bookkeeping, virtual assistant services, ePublishing, healthcare, document management to complex software development projects, to name a few, are being outsourced to third party companies that have the capacity to undertake such specialized tasks. A word of caution though, before you can decide what to outsource, list out your company’s strengths and weaknesses. Your strengths are your forte and it is better they are handled in-house. 

Categories of Tasks to outsource

Outsourced tasks can be broadly classified in three different categories. On the top of the list are tasks that need high levels of a particular skill that are currently handled by the top executives in the company. Next is the category under specialized tasks like computer programming, software development and IT support. The third category comprises repetitive tasks like data entry, data mining, inventory, accounts payable and more. Today, the services of a virtual assistant are most sought after, as the Internet has made the globe a smaller place, with distance hardly making any difference.

Some of the most common jobs/tasks that people outsource for are mentioned below: 

1. Web development and IT support:

A freelance IT team means that you don’t have to worry about data protection, software implementation, network system setups, and troubleshooting, etc. Are all handled by the outsourcing team? Apart from that, you’ll have access to IT resources that you have in the house, without additional cost.

2. Customer support:

Customers are the most vital part of any business, and it requires the utmost attention. If you outsource your customer support desk, then your customers will have access to reach out to you even when you’re not actually in the office. 

3. Consultation services: 

Consulting experts from time to time is good for business but bad for your bank account if you plan to hire one. Instead, you can outsource someone with the credentials at a lower price. You’ll only pay for what you need help with, not hiring them full time, so it’ll be easier on your pockets as well.

4. Payroll accounting:

Payroll and accounting are time-consuming. Although it’s done every month, your business will not be affected if you outsource. You can outsource payroll staff to handle taxes, compensations, and other procedures so that you can focus on operating your business.

5. Legal services:

Hiring a full-time company lawyer is not feasible for a lot of companies, especially small scale startups. It’s better and cheaper to outsource a legal team as and when the situation demands. 

6. Data entry:

Inventory, cataloging, and other repetitive tasks are better off delegated to an outsourced person or team. You’ll get the job done accurately and save time, money without wasting effort on your team’s part. 

This is just a small list of things you can outsource if you want. Companies are outsourcing complete projects, too. You just have to make sure that the benefits and costs of your investment are worth it. 

Your Ideal Partner

It is critical to take utmost caution when picking your outsourcing partner in order to establish a stronger business through outsourcing. One bad decision might ruin your reputation and inflict more harm than good. Today’s technology can assist you in finding a competent partner who can assist you in establishing your company.. While starting locally is a great idea, it pays to inquire around and check with other business owners who are already outsourcing their tasks. There is nothing better than going by word of mouth and you may also seek your partner through professional online networks like LinkedIn and Twitter. Outsourcing is the proven way to expand your business globally and small, medium and large businesses can outsource their work and save a whole lot of money in the process.

 At TagX we offer a number of outsourcing services like data processing, data entry, data conversion and image processing. Do you plan on outsourcing your work to save costs and receive it on time? Get in touch with our experts today and we’ll help minimize your workload.

Businesses are expanding at a rapid rate. The digital revolution has opened up a world of endless possibilities. Businesses are dealing with more data than ever before. The cycle of data management and maintenance is crucial to the proper operation of a business. Data entry projects are increasingly being outsourced to overseas organisations. These outsourcing companies can manage the entire data management operations. They follow a system that brings credibility and stability. Thus, to lessen the workload and concentrate on other core business activities, many companies outsource the work to companies who handle non-core data entry jobs.

Outsourcing data entry jobs will help increase your work rate. But the real struggle is finding the right company to handle any project given to them at any time. Here are a few tips to help you decide which company to hire that will handle your job quickly and efficiently.  

1. Services Offered

Once you have decided to outsource any work, you must check if the company has the workforce and infrastructure that is required to handle different tasks related to data entry. It saves a lot of time, money and resources when you assign a project to an outsourcing provider and also the quality of work is likely to be excellent.

2. Turn Around Time

In a fast paced world like ours, everyone wants everything to be done quickly. Likewise, when you outsource any project you typically want results that are not too delayed. First, you must determine if the outsourcing company can handle any size project. Checking their TAT will help you decide whether they are a qualified and professional team. If they operate round the clock it will be a bigger plus for you as well.

3. Flexibility and Scalability

Every business today has to be dynamic and adaptable to changes and hence, your outsourcing partner needs to be highly flexible. Flexibility should not be limited to the rate of workflow. It should encompass the type of data and timing of operations. Further, the partner should support scalability and allow for the growth of your business. This requires the partner to have the infrastructure and skills for capacity building and expansion.

4. Cost-effectiveness

Many providers portray their prices as one and bill you for a different price altogether. Not all providers can offer cost-effective services that are of good quality. It is vital to take cost into account while looking to hire any outsourcing company. Once your company begins its growth, you can spend more to match the services you require. But you must be mindful of outsourcing costs and keep them as low as possible.

5. Financial Status

Before you hire any company, be sure to check their working capital and financial background to determine if it is stable. By doing this, you can ensure your work will be done in a timely and efficient manner. In case your project is a large one, you must undertake a risk assessment before you enter into any sort of agreement.

6. Data Security

Data security is probably the most essential aspect required from both a business and legal perspective. You must always check the protocols and policies undertaken by them to ensure data security. Having an ISO certificate will always prove good for any company. Inquire about NDAs and whether CCTV cameras are installed in their office.

Wrapping Up

Data entry is probably the most common job that requires outsourcing companies. At TagX we offer a number of services related to document management like data processing, data entry, data conversion and image processing. Do you plan on outsourcing your work to save costs and receive it on time? Get in touch with our experts today and we’ll help minimize your workload.

Augmented Reality (AR) is a digital media that allows the user to integrate virtual context into the physical environment in an interactive multidimensional way. AR software derives information about the surrounding environment from cameras and sensors. Implementing AI enhances the AR experience by allowing deep neural networks to replace traditional computer vision approaches, and add new features such as object detection, text analysis, and scene labeling.

AR and VR are often discussed interchangeably or in the same circles, but it’s important to distinguish their differences.So first, let’s establish what these terms mean.

• AR: Augmented Reality – this is any technology that augments your real life experience with the digital one – you can show your kids a wombat on the floor of their room through the screen on your smartphone.
• VR: Virtual Reality – this term is used to talk about fully virtual experiences, with the help of special goggle-like devices.
• AI: Artificial Intelligence – AI is different from VR and AR, because it doesn’t work on the level of user’s perception ,it is a technology under the hood of the product you use. This is how Spotify knows what to play next after your favourite song. It is the gathering and processing of vast amounts of information to make the user experience better and tailored to the user.

Power of Augmented reality with Artificial Intelligence 

Augmented reality (AR) is quickly becoming one of the biggest game-changers for businesses, profoundly transforming brand engagement.To create these compelling and powerful customer-centric experiences, AR doesn’t act alone. The underlying technology that makes the new dimensions and immersive experiences possible is artificial intelligence (AI). 

AI is the key to enabling AR to interact with the physical environment in a multidimensional way. Object recognition and tracking, gestural input, eye tracking, and voice command recognition combine to let you manipulate 2D and 3D objects in virtual space with your hands, eyes, and words.

AI enables capabilities like real-world object tagging, enabling an AR system to predict the appropriate interface for a person in a given virtual environment. Through these and other possibilities, AI enhances AR to create a multidimensional and responsive virtual experience that can bring people new levels of insight and creativity.

Types of Data Annotation in Augmented Reality include:

1. Object labeling

Object labeling utilizes machine learning classification models. When a camera frame is run through the model, it matches the image with a predefined label in the user’s classification library, and the label overlays the physical object in the AR environment. For example, Volkswagen Mobile Augmented Reality Technical Assistance (MARTA) labels vehicle parts, and provides information about existing problems and instructions on how to fix it.

2. Object detection and recognition

Object detection and recognition utilize convolutional neural network (CNN) algorithms to estimate the position and extent of objects within a scene. After the object is detected, the AR software can render digital objects to overlay the physical one and mediate interaction between the two. For example, IKEA place ARKit application scans the surrounding environment, measures vertical and horizontal planes, estimates depth, and then suggests products that fit the particular space.

3. Text recognition and translation

Text recognition and translation combines AI Optical Character Recognition (OCR) techniques with text-to-text translation engines such as DeepL. A visual tracker keeps track of the word and allows the translation to overlay the AR environment. Google Translate offers this functionality.

4. Automatic Speech Recognition

Automatic Speech Recognition (ASR) uses neural network audiovisual speech recognition (an algorithm that relies on image processing to extract text). Specific words trigger an image in the library labeled to fit the word description, and the image is projected onto the AR space. An example is the Panda sticker app.

Importance of Data Annotation for AR

Innovative AR and VR experiences start with high-quality training and validationWhen it comes to overcoming AR and VR challenges, quality AI training data matters.  98 percent accuracy with semantic segmentation is needed to even remove the background for an AR application. And, without a precise understanding of motion or accurate perception of the environment, the realism of AR and VR applications is lost, and the user’s experience is greatly impaired. For example, before you can eliminate hand controllers, you need to first understand what your hands and fingers are trying to do i.e. point at something, grab something, wave at someone, etc., and collect data relevant to that use case. 

Everything from localization and mapping, the way computers visualize the world, and semantics such as how computers understand the world as we do are all concerns that must be addressed for production level AR and VR. This is where the quality of your training data  makes a difference.

TagX offers data annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions. With our high-quality training data, you will create the accurate AI models needed to provide a seamless and authentic AR/VR experience.