Tech posts

Training your ASR Model: Why Live Data is the way to go

Training automatic speech recognition (ASR) models has always been a challenging task in the field of machine learning. ASR systems are designed to convert spoken language into written text, enabling various applications such as transcription services, voice assistants, and voice-controlled systems. While there are multiple approaches to training ASR models, recent advancements have highlighted the immense benefits of incorporating live data into the training process.

Automatic Speech Recognition (ASR) has become an essential application of artificial intelligence. Its use cases are copious, from voice assistants to call centers to tools to help the deaf and elderly. ASR systems rely on large amounts of training data to learn to recognize speech accurately. There are different types of data that can be used to train ASR models, including but not limited to scripted monologue speech, simulated conversations between humans or between and human and a machine, and live recordings. All of this data can be found in various domains, languages, and locales in the marketplace, the largest online marketplace for ethically sourced AI training data in the world. With the recent addition of live data to our marketplace, we wanted to explore why it is so valuable in comparison to simulated and scripted data.

What is Live Data?

Before discussing why live data is valuable for training ASR models, it is essential to define what we mean by live data. Live data is speech data that is collected in real-life situations, such as phone conversations, meetings, or speaking to smart devices. This data reflects the variability and naturalness of speech in real-life situations and is considered the most relevant and authentic type of data for training ASR models. Live data is often preferred over scripted or simulated data because it provides a more accurate representation of the acoustic environment and the way people speak in real-life situations.

While live data can be considered the most valuable type of data for training ASR models, it can also be the most challenging to gather. Gathering live data also requires ethical considerations, as the data can require consent to collect and may contain sensitive information. As a result, many researchers and developers forgo it altogether. However, at, efforts are being made to gather live data in an ethical and informed manner, such as obtaining explicit consent from speakers and anonymizing the data to protect privacy. These efforts are beginning to pay off.

Why are we so excited by live data?

In many cases, training ASR models on live data has been shown to have several benefits over those trained on scripted or simulated data. Here are some of the reasons why live data is so valuable:

1. Variability in speech patterns

Live data provides a wide range of variability in speech patterns, including different accents, speaking styles, and background noise. ASR models trained on live data are more likely to be able to recognize speech accurately in real-life situations, where there is often a lot of variability in the acoustic environment. This variability can be challenging to capture in scripted or simulated data.

2. Naturalness of speech

Live data is more natural and reflects how people speak in real-life situations. In contrast, scripted and simulated data can induce the Hawthorne Effect, where speakers modify their speech patterns when they know they are being recorded or observed. This can result in speech data that is less representative of the natural variability found in real-world scenarios. On the other hand, live data provides a more authentic representation of speech in real-life situations, capturing the naturalness and variability of speech that is essential for training robust and accurate ASR models in real-world scenarios.

3. Relevance to the application

Live data can be more relevant to the application being developed. For example, if an ASR system is being developed for a call center, live recordings from the business itself can be used to overfit the ASR model to the specific use case of a call center. This data will be more relevant to the application by providing more realistic examples of, among others, accents, prosody, and pronunciations of domain-specific words.

4. Quality of data

Live data provides a higher quality of data than scripted or simulated data. The recordings are usually of higher quality because they are made in a real-life situation, rather than a controlled environment. This higher quality of data can lead to ASR models that are more accurate and more effective.


In conclusion, live data is a valuable resource for training ASR models, providing an authentic representation of speech in real-life situations. While gathering live data can be challenging, the benefits of using it for ASR training are clear. ASR models trained on live data have been shown to outperform those trained on scripted or simulated data, achieving higher accuracy rates and better performance overall.

If you are looking to train an ASR model, consider incorporating live data into your training dataset. At, we specialize in providing high-quality, ethically-sourced live speech data for machine learning applications. Our dataset is anonymized, and by using our live data, you can train your ASR model with the most accurate and natural speech data available.

Start your journey to better ASR performance today by visiting our website and exploring our live data offerings.


Leave a comment

Your email address will not be published. Required fields are marked *

Terms of Use agreement

When contributing, do not post any material that contains:

  • hate speech
  • profanity, obscenity or vulgarity
  • comments that could be considered prejudicial, racist or inflammatory
  • nudity or offensive imagery (including, but not limited to, in profile pictures)
  • defamation to a person or people
  • name calling and/or personal attacks
  • comments whose main purpose are commercial in nature and/or to sell a product
  • comments that infringe on copyright or another person’s intellectual property
  • spam comments from individuals or groups, such as the same comment posted repeatedly on a profile
  • personal information about you or another individual (including identifying information, email addresses, phone numbers or private addresses)
  • false representation of another individual, organisation, government or entity
  • promotion of a product, business, company or organisation

We retain the right to remove any content that does not comply with these guidelines or we deem inappropriate.
Repeated violations may cause the author to be blocked from our channels.

Thank you for your comment!

Please allow several working hours for the comment to be moderated before it is published.

Director of Strategic AI Partnerships