Artificial intelligence (AI) can have a significant positive impact on your business operations, but only if done right. Every successful AI project is brought to life through carefully thought-out and executed stages. The effort you put into defining your use case, sourcing data, building and fine-tuning your machine learning model will ultimately determine its performance. So before you kick off the next AI project in your company, here is what you need to consider.
In other words, where to begin? The first step in AI development is defining the use case. A strong start will help determine and set the tone for what you are trying to achieve and align your team’s efforts. You must have a clear goal:
If you can answer the questions above with confidence and clarity, it will help you scope the project and get on the right track from the get-go.
Remember to use an iterative approach by breaking down your work into smaller steps. For example, to build a customer service agent, start by creating a solution that forwards customer contact information to the right person before trying to have the agent solve the problems independently.
Read more: Integrating AI into your business process
Data sourcing is by far the most crucial stage in AI development. How well (or not) your machine learning algorithm will perform will depend on the training data and its annotation quality. You want to avoid the unwelcome ‘garbage in, garbage out’ scenario.
Start by defining your process for getting the necessary data. Continue with the iterative approach and divide this process into smaller steps:
Similarly to data sourcing, AI model building and training should be a step-by-step process. Consider aspects such as:
If you’ve got the above figured out, define the features of your model. Unnecessary features can negatively affect accuracy, so you should only use components relevant to the model and your use case. Use tools and algorithms to help measure and remove unnecessary model features when needed.
For easier result measuring, ensure that each model iteration is versioned and compared using the same data. Plus, when choosing the algorithm, consider if it will allow you to interpret the output without jumping through hoops.
“AI model accuracy is closely connected to the quality of training data. Be specific and clearly define the steps you will take to collect data. A well-thought-out process for data sourcing can shorten your project by half a year.” - Thomas Forss, Co-founder and CEO, StageZero Technologies.
During the testing phase of AI development, you want to have some gold standard data to test against. Gold standard data is your perfect, correct dataset. Test each version of your model against this validation data to see how you are progressing, and keep iterating until the model performance improves.
Edge cases, in particular, are where AI models struggle. Edge cases are rare happenings for which no data in the training dataset exists, such as, for example, a bird covering the license plate just as a truck drives past the gate. You will need to add more data to resolve such cases, and these cases will be hard to imagine before the product is live, which is why iterative development is recommended.
In speech recognition, one of the most common reasons models underperform is forgetting to include voice data with different accents. If a model is only trained with British English natives, it may not work when someone is speaking English but with a Spanish accent.
How much should you test? It will depend on your use case. If we take a chatbot, 70% accuracy may be enough, but if it is a self-driving car - you want to aim for 100% for obvious reasons. The main goal of testing is to prepare the model for deployment.
As you prepare your AI model for deployment, consider the most efficient way to do so. Some projects might face regulatory issues. For example, because medical records in Finland cannot be processed elsewhere, a healthcare project may have to accommodate this.
In most cases, you can use a cloud provider to deploy your AI model. But if the project is large-scale, it might make sense to build your own server infrastructure or use a bare cloud solution and build your setup on top. There can also be examples when you might only deploy locally: perhaps the model contains particularly sensitive company information or has no reason to be connected to the internet.
Evaluate whether you have achieved your KPIs and the goal of the AI project. If set parameters are not met, adjust/replace the model or improve the quality/quantity of the training data. Upon meeting all defined parameters, deploy the model into the intended setup.
Set up a monitoring system to ensure your model is working as intended. A continuous model iteration is needed to respond to technology, business, or data changes. Regularly test output against the gold standard data and update the model with new data to ensure it still fits the use case.
Expect that the model will behave differently when deployed in the real world. Pay close attention to irregular decisions or deviations from the pre-defined accuracy of the model. When the model fails above your set threshold or does not adhere to the set parameters, make necessary adjustments and fine-tune for optimal results using new data.
If you have decided on the use case, StageZero Technologies can help you with the next step - data sourcing. We offer an entire library of pre-collected and custom speech datasets.
If you are still trying to figure out what you need, reach out to us, and we will help you assess which datasets would work best for your project.
Most companies today use real-world data for artificial intelligence (AI) model training, but it can be lacking, especially for smaller businesses or new products. And even when real data appears to be abundant, data scientists often meet limitations as AI model training begins: real data may be biased or unusable due to privacy concerns. Synthetic data can solve some of these issues—often in a more cost-efficient way—but it has a few drawbacks of its own. Many advanced solutions require a mix of real-world and synthetic data to perform well and handle edge cases. In this post, we’re comparing the pros and cons of both real-world and synthetic data.
First, real-world or real data represents genuine information collected in a real-life use case. It is data produced by real events or happenings. For example, it could be data companies collect through day-to-day customer service interactions.
Synthetic data is artificial computer-generated data imitating real data. Such data is created by data-generating programs. There are three types of synthetic data:
Now, let’s compare real and synthetic data in terms of cost, quality, annotation, scalability, privacy, and regulatory compliance.
Typically, it is less costly to source synthetic data, but it depends on the use case. For example, if you are training a machine learning algorithm for a self-driving car, it would be much cheaper to simulate a car crash than to create one to collect necessary data.
Larger companies may already have a lot of real-world data to work with, but these datasets are often imbalanced. This is due to edge cases that happen rarely and therefore are not represented in the data. Data scientists then have to turn to synthetic data to supplement the shortage.
Creating synthetic data for text algorithms is usually cheaper as well. Existing data generators can generate grammatically correct texts of acceptable quality.
Meanwhile, synthetic data for speech algorithms is less frequently used due to the complexity of speech data. Today the only way to generate synthetic data for speech is to have real users simulate or act out necessary situations.
Privacy and regulatory compliance are the most significant drivers of using synthetic data in AI. Because synthetic data does not contain any sensitive or Personally Identifiable Information (PII), it makes it easier to preserve the privacy of real individuals and comply with regulations such as GDPR.
However, when synthetic data mimics real data that contains privacy issues or bias, they might be reflected in the synthetic data. It often happens with algorithms that generate synthetic data from biased historical data. For example, the demo of Meta’s language model Galactica was removed three days after going live because it produced heavily biased or faulty results.
Synthetic data can provide more diversity by enabling the inclusion of edge cases, while sourcing such cases from real data may take a lot of work. In terms of bias, both real and synthetic data can be inherently or historically biased because, as mentioned earlier, synthetic data mimics real data, absorbing existing vulnerabilities.
The overall quality of the AI model is dependent on the data source. If we take synthetic data, its quality level will be influenced by the data generator and the quality of the original data.
To reflect reality, synthetic data needs to be sufficiently accurate and diverse. However, excessively manipulating synthetic data to achieve a level of fairness might distort the end result – unrealistic data cannot provide reliable insights.
Data labeling and annotation can be a months-long, resource-intensive process. Here synthetic data has an advantage against real-world data because synthetic data is often automatically labeled as it is created. This also takes the human aspect of mislabeling. However, it can instead introduce faulty labels from the algorithms that create the data.
Data needs can change as you start working on your machine learning algorithm. One of AI development teams' more common problems is a lack of data. Without more training data, it is challenging to scale AI projects.
Synthetic data is a better fit regarding scalability because it is easier and cheaper to source, with a few exceptions; for example, high-quality synthetic speech data is typically harder to produce.
In the perfect scenario, you have plenty of diverse, unbiased real data matching the use case. Theoretically, real-world data is more likely to deliver higher performance. However, in practice, synthetic data helps with edge cases, fills data scarcity gaps, and alleviates privacy concerns.
For many use cases, it does not need to be a choice between synthetic and real-world data. Often, a combination of both will get you the best results. For most data scientists, the more critical factor regarding data is having enough high-quality, unbiased datasets.
If you are looking for data, StageZero Technologies offers hybrid speech data for conversational AI and intent classification data for speech and text use cases. Our data is produced by a global network of 10 million contributors in over 40 languages. Get in touch and tell us about your data needs.
Whether you want to develop a brand new or fine-tune an existing Automated Speech Recognition (ASR) model, you require speech data. From type of machine learning model to dataset specifications, there are a few things to consider as you look for speech recognition training datasets. Knowing the kind of data your project requires will help prepare better and improve the performance of your ASR model.
Assessing what makes one dataset better than another is not clear-cut because it all comes down to your needs. A good place to start would be to evaluate your use case and the design of the model you want to develop, which will determine the necessary type of speech dataset.
Identifying which voice datasets would be the best fit depends on the design of your machine learning model. Will you perform actions based on speech or will you first convert it to text? You could use voice data to train the algorithm to perform actions or you can perform actions based on transcribed text.
For example, identifying sentiment directly from audio makes more sense. It is much more challenging to recognize nuance in speech if it is in text form because there is a lot of extra information in how we speak.
For machine learning algorithms to detect sentiment without human input, they need to be trained beyond what is said and understand subtleties such as sarcasm or puns. Plus, people can speak with excitement or anger; they can speak very slowly or fast - all of it gets lost if audio datasets are transformed into text.
The same goes for algorithms that try to identify intent; audio may provide more context. At the same time, it does not have to be text or audio; combining both might be the best way to go. The critical element here is how you will design the model to make it easier for you to manage it in the future.
The design of your ASR model will also influence the specifications of needed speech datasets. When it comes to audio data, everyone has different requirements regarding background noise. To give some examples:
So, should there be some noise or no noise at all? Again, it will depend on your use case. Some want the background noise to be as natural as possible, be it a police siren or chewing sounds. Some prefer speech datasets with zero background noise, including breathing. Those who choose speech recognition datasets without any noise usually expect that this way, their machine learning model will require less data to train, as in less noise - less distraction, and therefore less data.
For example, let us say you are building a speech recognition model for customer service: a person reaches out to cancel their account while a motorcycle is heard driving by. The algorithm might learn that a customer's account needs to be closed whenever a customer talks and a motorcycle sound is heard. In other words, it associates motorcycle sounds with intent to close accounts.
However, the more speech data is fed to the algorithm, the more it learns, regardless of how much background noise is included. Some studies show that doubling the amount of data improves output quality.
So in the motorcycle case, the algorithm would eventually learn enough to ignore any noise, including vehicles driving past. And then you have an algorithm that is trained on real world data which generally ends up performing better, however, usually requires more training data to get started.
An ASR model can only be as good as its training data, so aim to get lossless speech data. Your audio datasets should not be compressed in any way that could reduce their quality.
Most audio compression tools create a loss in the original data file, so avoid compression altogether or use a compression method that can preserve all of the original data. To the human ear, a compressed audio file might sound the same, but even minor changes can significantly impact an algorithm.
Another aspect to consider as you browse for speech datasets is the microphone. Check the information on a given voice dataset and see if the microphone matches what your end users are using.
For example, if they use a phone, the speech data should be recorded with a phone. If they use headsets or laptop microphones, this should match as well. Avoid using phone recordings for use cases where a laptop microphone is used and vice versa.
Additionally, listen to the speech recordings and see if they are choppy. Make sure they are continuous and contain no technical issues. Usually, a sample for each dataset is provided for you to listen to and make an informed decision.
A few hours or thousands of hours? It will depend on your use case and the technology you are working on. You might need hundreds or thousands of hours of speech recordings if you are developing a new technology - your own speech model. But if you are building on top of existing technology, such as Alexa, then a few hours might be enough.
A lack of data diversity can lead to bias and poor model performance. Your chosen speech dataset should be balanced and represent gender, race, or class, among other aspects.
The recordings should reflect the diversity of the general population or if your use case is specific to a group of people - it should match that group of people. The training data should match as closely as possible to the end users. Consider your target age group as well and ensure a proportionate ratio of male and female voices.
Speech data can be pre-made and ready to be downloaded as-is or collected based on your custom requirements. Choose your best fit based on your use case, needs, and preferences. In many cases, ready-to-use speech datasets can speed up conversational AI, ASR, or Interactive Voice Response (IVR) projects.
StageZero offers an entire library of pre-collected and custom speech datasets. Our speech data is 100% human-verified and fits many specific use cases.
If you are still trying to figure out what you need, reach out to us, and we will help you assess which datasets would work best for your project.
The speedy growth of artificial intelligence (AI) has exploded its impact on both the business and social worlds: from Natural Language Processing (NLP) to automotive and facial recognition and beyond. This expansion leads to a high need for diverse data, and the fact that the industry is lacking it is becoming a serious problem.
AI models are always hungry for data. Emergent types of AI include life-long learning machines which are being built to obtain data constantly and permanently. A stable source of data helps better guarantee satisfactory results.
Nevertheless, while data is becoming increasingly important and machine learning (ML) is commonly used to make predictions for businesses, the struggle with data bias is also turning into a growing concern. Biased AI models, especially when implemented at scale, can bring social risks.
Data is the starting point of ML. From numbers and text to photos – numerous types of data are collected from various sources and then used as training data, the information which the ML model will learn from. The more diverse the training data is, the better the model will perform.
Even though ML models can contribute to the success of an AI company using data assets to leverage products and results, it is important to keep in mind that they will always just be as good as the data they are trained on. This means that if the data is not diverse enough, or is not well-processed, the algorithms can face a problem called overfitting – the model learns the noise and disturbing details in the ‘low-quality’ data which results in negative performance of the model.
To put it another way, when the data does not meet diversity and quality, the ML models will perform good results on the data they have been given in training, but poor results on new data. As the importance of data diversity and quality is increasing especially in the field of data science and cybersecurity, it is critical to avoid even minor errors.
Besides vast amounts, data also needs to have good ‘qualities’: accurate, clean, balanced, and representative. Bias happens when the training data does not represent fully the diversity of a general population. A dataset can be bias toward, for instance, a certain gender, race, or class, and has a very high chance of creating an inaccurate model as a consequence.
Algorithms nowadays play an important role as they are being increasingly utilized to lead critical decisions that have impact on substantial groups of people. Therefore, it is crucial to set up specific safeguards to detect and fix errors of bias in AI and ML. Often occurring as an accident, it is hard to notice bias right away. Indeed, some can even go unnoticed for a long time. Specialists will have to carefully assess prediction results to detect bias from a model.
Data scientists have to be cautious in data collection and annotation stages to ensure that training data is diverse, balanced, and ideally covers corner cases. It is essential to accomplish balanced and representative training data from a global subject pool if the model is eventually applied to a global data pool, especially in cases that concern human populations such as facial recognition or sentiment analysis.
For instance, a facial recognition algorithm that only learnt facial features of the Western population will likely struggle with recognizing African or Asian populations, giving inaccuracy, or will misclassify; or an internal recruiting application that had not been given diverse data will likely be partial to one gender or race. Well-known examples include when Amazon’s internal automated recruiting system discriminated against female candidates, Google Photos’ facial recognition identified African-Americans as gorillas, and Facebook AI put a ‘Primates’ label on video of black men.
In short, data has a substantial influence on the success of technology. Delay and carelessness in tackling data bias will consequently lead to low-quality AI models and negative results.
It is a moral responsibility for AI companies to address and tackle data bias, not just for customers but also for their own good. For this, data diversity is a solution.
The most practical approach to address and solve data bias is to actively tackle it at the data collection and curation phase. Algorithms have the ability to circulate or even magnify biases in their data sources. Diverse data should be used here at this point to diminish bias.
To ensure that the data is adequately diverse and proportionally accounts for all variables, there needs to be concrete procedures in place. Companies that operate internationally should establish a system that includes a step of analyzing data from all its functions to generate a new procedure before adopting AI. Especially, businesses that specialize in NLP or computer vision should pay attention to this process since it will simultaneously enhance product quality and market access.
Data collection and curation should ideally be executed by a diverse team: diverse age, gender, race, ethnicity, backgrounds, experience, and points of view. Such a diverse team together can contribute to better consideration and prediction of different business use cases and scenarios.
Last but not least, a type of diversity that is not popularly mentioned but is still essential and relevant: intellectual diversity. From education backgrounds, political views to teamwork style – our unique individual characteristics not only help to boost a team’s creativity and efficiency, but also to enhance the ability of identifying and correcting bias.
It has always been a substantial challenge to evade bias entirely but with well-planned awareness and strategy, minimizing its existence is possible. After detecting a bias, data scientists have an important task to balance it. Common methods include relabeling samples, adjusting sampling distribution, modifying confidence weighting, and executing new mitigation strategies.
It is important to be aware that dealing with data bias is a much more challenging task than detecting and collecting it. This task should be human-in-the-loop as it cannot be simply automated. Here, data scientists or engineers need to examine distributions thoroughly, in order to identify unusual activities. Human force involves considering factors that cause odd correlations, such as gender, race, and age, no matter whether these variables are inputs to the model.
StageZero have got you covered here, with 100% human-verified data for a range of specific use cases, we ensure that bias is maintained at a minimum to support your model’s accuracy. To learn more about how we can help you, contact us. Keep up with our most recent developments by following us on LinkedIn.
This September StageZero visited the Conversational AI Summit London, a two-day summit for key players in the conversational AI ecosystem to discuss the future of conversational AI across principal industries such as e-commerce, telecoms, and banking. Apart from the high of being back at face-to-face events again, we had the opportunity to speak face-to-face with the stars of the conversational AI industry, including key players from Meta, Bosch, Amazon, Google, the BBC and more. Here we share the top 5 key insights we came away with, and what they mean for the future of conversational AI.
The principal challenge for conversational AI comes down to handling languages other than English. Most of the presentations over the two days focused or mentioned this as a key problem that even enterprise-sized players are struggling to handle successfully. The main reason for this struggle comes down to a distinct lack of data for languages other than English. While data for lower resource languages is scarce, the market demand is remarkably high. One presenter cited receiving tens of emails consistently on a weekly basis for products in languages other than English, and they’re incapable of satisfying this market demand due to the lack of data availability in such languages. This sentiment was echoed from other key players at the event who are also struggling, not only with the quantity of available data, but also with the quality of the small amounts of data that is out there.
As the market continues to grow and the benefits of conversational AI become increasingly familiar to customers, the customers are becoming more demanding when it comes to localized conversational AI solutions. The key players in this field understand that in order to satisfy the market, they’ll need to secure a high volume of varied and reliable data across a plethora of lower resource languages. They understand this because their customers remind them about it on a daily basis. The resounding theme from the enterprise presentations was one of preparation for this demand, since it is expected to grow dramatically. Therefore, industry leaders are already securing solutions for obtaining quality data especially for European languages such as Romanian, German, Italian… One presenter explained that their chatbot is being extended now into 16 more languages, and their bots must understand at least 80% of their customers’ speech in order to be considered functional. This requires a high amount of quality data, and it’s estimated that demand for such data will increase drastically over the next few years.
What does this mean for the future of conversational AI? As the market develops quickly, more and more solutions will be available and more usable in localized languages across the EU, extending benefits which are currently restricted to English language out to the rest of Europe via applications in customers’ native languages and dialects. This will allow industry leaders to tap into an eager and underserved market and ensure high ROI on their projects.
While the old challenge of combining letters and numbers in English has been mostly overcome, this challenge is still running strong when it comes to other languages. Most of the companies presenting at the summit deal with some sorts of codes regularly and this provides an ideal nightmare scenario for their conversational AI implementation outside of English.
Such codes can be for example a post code or a customer identification number, or even a combination of letters and numbers found in addresses (like “apartment 25 A, 62 Acorn Street”), and are used in almost all of their automated conversations. Critically, such codes are often used for customers to authenticate their identification at the very beginning of their call – therefore successful implementation is crucial.
For the success of their conversational AI project, it is critical for companies to be able to handle such codes and combinations accurately and quickly. Today, this is not a possibility for the majority of the key players in the industry – even the trailblazers are banging their heads about this one. They’re spending unnecessary amounts of their project budget and wasting time and energy on testing and retesting such issues, while the solution is relatively simple, and comes down again to the availability and quality of training data.
Since this data is difficult to source for languages other than English, efficient training of the conversational bot is tricky indeed. As it happens, this is exactly the problem we solve at StageZero, by sourcing such data quickly and at low cost. Contact us to find out more.
Interest in the room peaked during the presentations about more realistic sounding conversations, and the progress made here recently. The improvements have been ground-breaking and are set to continue at least at this momentum.
From a user perspective, the principal benefit of large language models (LLM) in conversational AI is the ability of the machine to make the conversation feel much more natural to the user. Other technology such as Google’s WaveNet neural network allows for smoother audio processing and even using speech disfluencies to create more realistic sounding voices.
The advances made in such technologies have enabled trends like taking a step away from a robotic-sounding voice, towards a more natural human-sounding one. The market seems excited about the potential here. Companies are designing personas for their bots, and selecting carefully curated voices to match the persona. This can involve choosing a gender, an accent, and even a specific vocabulary to match the persona of the bot, leading to a conversational experience that more closely resembles that of speaking with a real human. A couple of industry leaders showcased bots with distinct types of voices for different situations such as “newscaster”, “storytelling,” and “customer service”.
Google Duplex is a prime example of how this can be taken to the next level, with their bot using disfluencies like fillers such as “uhm” and “umm” matched with a voice tempo that is more suited to that of a real human. Examples of Duplex went viral already in 2018 when the bot was used to call companies to verify their covid-era opening hours, and listeners were surprised by the realistic feel to the conversations.
In parallel to the amazement came concern about the ethics surrounding such technology, and these concerns persist today. Day two of the summit saw a panel discussion exploring questions around the psychological impact of using a realistic voice and persona, and what this holds for the future of humanity. Largely it was agreed that the bot should inform the human that it is a bot. Most of the participants in the room said they felt uneasy at the prospect of discussing with a human-sounding bot, but that in the next five years they would probably get used to it. Companies such as the BBC were using human-sounding bots already, and they didn’t always specify that it was a bot – however, crucially, their bot was used for text-to-speech rather than for conversational interactions, which impacted people’s perception significantly.
Overall, there seemed to be an eagerness to explore the potential of realistic conversations and the technologies related to their implementation, while keeping privacy and ethics at the forefront of the conversation.
Basic questions and answers are pretty well covered nowadays, especially in English. But as users become more demanding, we need something bigger to wow them – and multi-modal conversational AI holds the key. Multimodal brings conversational AI beyond the basics and incorporates intent, context, and personalization into the conversation, resulting in a more natural and empathetic conversation for the user.
Sounds futuristic, right? But the industry leaders are already there, and their bots are ready to participate in discussions with users on a level you might not have expected at this stage. During the summit we watched demonstrations of conversational bots taking turns in conversations with humans, even in multi-speaker conversations. Systematically the bot demonstrated implicit understanding of who was talking to whom.
Such systems rely on a compilation of different AI systems working in harmony to produce unified output. Computer vision studies the body language of the human participants to better understand who is talking to whom. High volumes of speech data allow clearer understanding of who is speaking to whom, at what time the expected response should come, and what it should be.
Not only that, but emotion AI is also clearly on the rise. Companies demonstrated their bots performing a plethora of tasks, and while the bots mostly empathize through lexicon for now, many leaders explained that they’re starting to teach tone and mirroring to their conversational AI applications too. This will enhance the user experience even further as well as avoid inappropriate responses from the bots. In order to fine-tune such delicate projects, a high amount of sentiment analysis training data is required, so we were happy to validate this market development since that’s exactly where we shine.
Over the two-day summit we noticed a clear and consistent trend in the lack of availability across the board for conversational AI data in low resource languages. This can be attributed to several factors, the obvious one being the sharp growth in the market demand, which is scaling quickly outside of English-speaking countries. As native speakers of low resource languages learn more about the benefits of conversational AI applications, they naturally want to have access themselves. This requires localization of the application, which requires high volumes of good quality training data.
As conversational AI grows as a field, the experts in the field have started to notice patterns and trends in their projects, in their customers’ projects, and in the competitors’ projects, and are particularly interested in roadblocks and how to resolve them. Many of the roadblocks that were presented at the summit can be solved by good quality training data, for example the issue around codes mentioned above. This issue was cited consistently by several key industry players and especially in relation to languages other than English.
The summit provided us with solid validation that our technology is at the forefront of the conversational AI revolution. Indeed, our estimates show that we have the largest network of annotators in the world, which makes it particularly easy for us to solve issues relating to low-resource languages.
The Internet is full of datasets that cover all the possible areas of machine learning (ML): from well-known benchmark datasets to Kaggle competitions and data from academic competitions, the list goes on... The amount of datasets increases year on year. However, there are still not enough out there to build a high-quality ML-based solution.
In this article, we consult with Nikolai Zenovkin, Lead Data Scientist at StageZero Technologies, to uncover reasons why you may need more data in particular areas, and to look into what kind of resources are out there to help you to obtain the relevant data.
There is a significant need for the building of sustainable and reproducible experiments in scientific research, and publicly available datasets play an important role here. Therefore, the true goal of public domain data is to create a comprehensive benchmark for different kinds of models rather than to serve the goal of building high-quality production systems.
A model that shows state-of-the-art results on a benchmark can almost guarantee that it will suit your particular subdomain similarly well compared to other models that were trained on the same data. What it cannot guarantee however, is whether it will work well enough for your own subdomain and individual circumstances.
For example, if you are developing a fitness app that tracks exercises, you may see quite a different picture in occurrences in COCO (Common Objects in Context) datasets: from angle poses to light - everything will be different. Thus, you may not expect the accuracy to be anywhere near the values mentioned by model authors. In order to obtain higher accuracy, a good dataset would be needed.
Usually when companies kick-start a new ML project, they either already have the data they want to process, or already have a plan to train a model that fits their needs. In many situations, the team will require pre-collected datasets.
Some such situations include:
The biggest datasets contain less than 3000 hours of speech data. That may sound like a lot, but when we compare this to human speech learning we quickly understand this need. On average, a human hears up to 30,000 hours of speech before the age of 18. Hence, there is no such thing as sufficient speech data, indeed, even an additional 10 hours can improve the speech recognition model considerably.
Depending on the project, some teams might even need to collect specific data to recognize particular intents or activation words, but overall, all and any speech data can improve voice recognition performance to impressive extents.
Custom computer vision applications usually require custom data to work well. But sometimes having some data pre-collected is necessary to ramp-up projects and allow companies to penetrate new markets.
For example, to extend a license plate recognition system to a new target market in another country, the team might need to use images of license plates from the new target country first.
Extending an existing solution to new cases and markets in general is probably the most common case to buy ready-made datasets when it comes to computer vision.
The need for pre-collected data is clear, yet sourcing such data can be a lengthy and expensive process. Companies need to ensure that the quality of the data is high, bias is maintained at a minimum, and turnaround on the data delivery is swift. StageZero have got you covered, with 100% human-verified data for a range of specific use cases, available for download here.