The world isn’t a fair place. You surely cannot expect every individual to understand the speech bits you throw at them. Yet, there are times when machines can be more reliable than humans. Machines that are data-fed and trained. Or rather curated as per the ethos of speech recognition.
Conversational AI is the new fad. From designing chatbots to digital assistants, there is a lot of scope for creating responsive Artificial Intelligence models to make life easier. And in most cases, organizations planning to create enterprise-grade Conversational AI solutions rely on machine learning tools like NLP (Natural Language Processing), ASR (Automatic Speech Recognition), and more— all with the right training data in place.
And that is exactly what we are going to discuss throughout the subsequent sections. The importance of speech recognition datasets and the best options to get hold of them.
Table of Contents
What is speech recognition?
The ability to empower a machine to identify, assess, and even respond to human speech and sounds is termed speech recognition. And speech recognition is an important sub-domain of conversational AI, a machine learning-inspired tool that relies heavily on natural language processing and optimal data collection
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Where to find speech recognition data?
Before we take a closer look at the data sources, it is important to understand the usage patterns. For instance, you can ensure that the training data is used to train general speech machine learning algorithms or even narrow speech models that are usually restricted to call center activities and more.
Once the approaches and usage requirements are sorted, you can look at the following sources for procurement:
Proprietary Data (Customer-Extracted)
Arguably the easiest datasets to procure, anything proprietary concerns the organization behind the AI model in the first place. The company trying to develop a conversational AI product might have valuable and insightful customer data, fixed datasets, or anything relevant depending on the use case. However, using proprietary data needs you to take care of the legal regulations and user content.
Public Data
We sincerely hope that you know what data scrapping is! If not, consider this as a process where training data providers scan online resources like open-source whitepapers, research projects, and other options to get hold of usable datasets. Also, getting hands on public data makes sense if you are on a budget. Yet, unlike anything proprietary, public data sets require extensive pre-processing and quality checking before you can even put them to use for speech recognition.
Pre-packaged Data (Vendor-provided)
Yes, we know— there have been quite a few Ps in this discourse. Yet, we simply cannot do without pre-packaged data. Reason being: pre-packaged datasets are vendor provided; hence vetted. And if you are good at negotiating, it is possible to get hold of pre-packaged data at a competitive price.
While we are at pre-packaged data, custom datasets also deserve mention. These aren’t pre-packaged and processed but put together according to the organizational needs and use-cases. If you want your AI model to exude the highest level of customization, it is necessary to get custom data sets prepared from experienced vendors.
Wrap-Up
The hard truth is that the quality of your collected training data determines the quality of your speech recognition model or even the device. Therefore, it is necessary to connect with experienced data vendors to help you sail through the process without a lot of effort, especially when training a model or the concerned algorithms requires the collection, annotation, and other skillful strategies.
