If you want to build a reliable conversational AI system or a speech recognition device to use in your business, you need a lot of training data. High-quality speech data is crucial to properly test and train Natural Language Processing (NLP) models to ensure they will work as well as intended.
Otherwise, the results can be amusing at best and deeply frustrating at worst. Imagine an exhausted client trying to resolve their issue through an unhelpful voice assistant. Your speech recognition (also referred to as ASR or Automatic Speech Recognition) device must be powered by the right data to ensure a smooth service and happy clients.
Hundreds of hours of audio and millions of words of text need to be fed into NLP algorithms to train them. The input must match how your typical customers would sound, which is where most ASR issues emerge.
It is possible to tackle speech recognition issues at the roots. Start by making sure you picked a good way to collect your data. Your chosen method will depend on your project needs and whether you are building a general or a narrow speech algorithm.
There are a few ways to collect speech recognition data for your chosen NLP model. Below we discuss the three most common sources to find speech recognition data: proprietary, public and vendor-provided.
The easiest way to get speech recognition data to build machine learning models is to look into your own resources. Your company may already have hours of valuable customer data.
Since these data sets are already there, they will not cost you a fortune, and chances are – they are already naturally tailored to your use cases. However, if you choose to go with your own data, user consent and legal regulations will have to be taken care of.
A large number of speech recognition data sets can be downloaded online. Some of these data sets are part of open-source research projects, and some are data scraped from sources such as YouTube.
Public data is a good option when you don’t have a big budget and need to quickly collect a lot of speech recognition data. At the same time, these data sets require extensive quality checking and pre-processing before use. They are only suited for generic speech recognition algorithms, will not work as well for specific use cases, and have limited language offerings.
Here you have two options: pre-packaged or custom speech recognition data sets. Pre-packaged datasets are immediately available as they are vendor-collected for resale as-is. These datasets are affordable and easy to collect but can't be customized or scaled.
Meanwhile, custom speech recognition data is for when you cannot find an existing data set to fit your needs. A data solutions provider will create custom speech recognition data sets suitable for the required use cases.
Custom data sets provided by a vendor offer a high degree of customization, are cost-effective and scalable. You can choose from different types of speech data, whether scripted or conversational. All legal requirements are usually taken care of by the vendor by default.
On the other hand, such data sets are primarily collected remotely from participants’ phones or headsets, so you cannot influence audio or microphone specifications and have limited acoustic scenarios.
|Speech recognition data||Pros||Cons|
No additional costs
May already fit your use cases
|Need to get user consent|
Have to comply with various legal regulations
Only possible if you have already collected data from users or customers
|Public||Quick data collection|
Don’t need a big budget
|Requires extensive quality checking|
Limited languages and often does not fit specific use cases
|Data vendor||Customizable and scalable|
Legal compliance is taken care of by default
Gets you the exact data that you need for your use case
|Fewer choices in terms of audio or microphone specifications|
Limited acoustic scenarios
Collected data will either make or break your speech recognition device. Minimize the risks by collaborating with a reliable data vendor. StageZero creates and collects data for you in any language and with the help of millions of global contributors.
Learn more about how to produce diverse, well-represented datasets with StageZero.