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Top 5 Speech Recognition Data Collection Methods in 2024

Updated on Jan 2
5 min read
Written by
Shehmir Javaid
Shehmir Javaid
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple.

He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers.

He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations.

He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.
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From automotive cars to healthcare diagnosis, speech/voice recognition applications are bringing advancements to many industries. Data is an integral part of developing and improving speech recognition systems since the overall level of system performance relies on data quality Apart from training, speech recognition systems regularly need to be improved with fresh data. Collecting or generating such data can be difficult, especially if the right method is not used.

In this article, we remedy this issue by exploring: 

  • What is speech recognition data collection?
  • How is it done?
  • What are the top 5 methods of collecting data for speech recognition models?

If you want to work with a speech data collection service, here is a guide to find the right fit.

What does data collection mean for speech recognition?

We have discussed data collection for AI/ML before. To summarize, it means gathering different types of data to prepare a dataset to develop or improve AI/ML models. For speech recognition, the type of data being collected is audio data, specifically speech data generated by humans. This data is gathered to train/improve models that understand and generate natural language. 

How is data collected for speech recognition?

Considering the scope of your project and selecting the right voice data collection method is one of the most important initial steps of the process. For in-house voice data collection, the following basic steps can be considered:

  1. Recruit contributors according to the use cases of the project. It is important to consider the language, dialect, and gender of the contributors before recruiting.
  2. Create a script for the contributors
  3. Prepare the equipment and environment specific to the required data and background noise.
  4. Record the data using microphones and other equipment
  5. Pre/post-process the gathered data to prepare it for audio annotation.

Now that the data is collected and annotated, it is ready for AI/ML training.

The basic process of collecting voice data in-house

Top 5 methods of collecting data for speech recognition models

This section will highlight the top methods of collecting voice/speech data:

1. Prepackaged voice datasets

Prepackaged voice datasets are suitable for developing and improving basic speech recognition models. They are ready-made datasets that are available online to purchase from different vendors.

1.1. Advantages

  • Cheaper as compared to in-house voice data collection
  • These datasets are large and can be purchased quickly
  • The quality is relatively better than public voice datasets since companies record these rather than the general public.

1.2. Disadvantages

  • Such datasets require significant pre-processing before usage, which adds processing costs to the budget.
  • They can not cover specific and unique use cases of speech recognition projects.
  • These datasets are not customizable/scalable, and additional data is difficult to add. 
  • Modern speech recognition models are becoming more complex, especially the ones that use deep learning.1 Prepackaged voice dataset can not fulfill such requirements. 

2. Public voice datasets

Pre-packaged datasets are similar to public datasets. The only differences are that public datasets are usually free to access and offer a much lower level of quality and specificity. The purpose of creating public datasets is to support innovation in the speech recognition industry.

3. Crowdsourcing voice data collection

If the company does not wish to go through the hassle of managing data collection, which itself is a project, then it can outsource crowdsourcing. If data is required in multiple languages and dialects, the firm can also work with a third-party crowdsourcing service provider specializing in data collection/annotation.

3.1. Advantages

  • You can customize and scale the voice datasets by specifying your requirements to the service provider.
  • Since crowdsourcing is done through an online application and the contributors use their own recording equipment, it can be cheaper than in-house voice data collection. 
  • Since crowdsourcing offers a wide range of contributors, which are spread across the world, voice data can be collected in multiple languages and dialects.
  • Third-party service providers also provide extra services such as pre and post-processing the audio data.
  • Since voice data is considered biometrics data, it is important to own it legally. Third-party vendors also transfer the rights of the voice data to help you avoid future legal issues.

3.2. Disadvantages

  • Since data is collected remotely through smartphones or other personal recording equipment of the contributor, there are fewer options in terms of equipment choice.
  • Sometimes, voice data is required with a specific noise in the background in order to train voice recognition to avoid that background noise. To achieve this, other types of background noise need to be removed. However, it can be challenging to clear the background noise as not all contributors have access to recording studios or soundproof rooms. Therefore, before working with a vendor, make sure it offers such specifications.

4. Customer voice data collection

This is another way of collecting voice data that is commonly used (see video below) by brands. These brands typically offer speech recognition-powered solutions, such as smart home devices or virtual assistants.

4.1. Advantages

Collecting voice data from your customers (users) can have many benefits:

  • The voice data collected from the customers is cheaper and available in abundance. There is only an initial cost of collection, but the rest is gathered as the customer uses the product.
  • Fresh voice data can be available in no time.
  • The voice data is meticulously precise to the use case since it is directly collected from the customers. This makes the voice data highly accurate.

4.2. Disadvantages

Some of the negatives of collecting customer voice data are:

  • As previously mentioned, voice data is a type of biometrics data, which is why collecting customer voice data has become controversial in the past few years. Due to privacy and security threats, customers are also usually not willing2 to share their voice data.
  • While collecting customer data, strict ethical and legal factors must be considered. Companies Like amazon in the past have gone through various lawsuits for collecting customer voice data without their consent.
  • Many countries are now imposing legal restrictions for collecting customer data which can make this method difficult to use. Learn more about data collection ethical and legal considerations in this quick read.

5. In-house voice data collection

Collecting in-house voice data can also be a way of creating high-quality and unique datasets. This method is suitable for projects which do not require large datasets in multiple languages or dialects.

5.1. Advantages

  • This method is suitable for secret voice recognition projects such as the military.3
  • They give more control over the voice data collection process, which means the developer can choose which devices to use and how to control the background noise of the recording.

5.2. Disadvantages

  • This method can be costly since it involves hiring contributors, purchasing recording equipment, setting up a studio (if necessary), etc.
  • This method can be difficult to collect diverse datasets
  • Since voice data is collected in real-time, doing it in-house can add significant delays to your project timeline.

Recommendations on which method to choose

Selecting the right method for your speech recognition project depends on the following factors:

Scope of the project

It is important to consider how big your project is. For instance, if a speech recognition system will be deployed in only one country, then pre-packaged or even public datasets can be used to train it. However, if it will be deployed in multiple countries and requires a dataset with multiple languages and dialects, then crowdsourcing would be a more suitable option.

Privacy level of the project

If the data in question is not private, the government of the country in which the data is being collected allows companies to collect data from its customers, then customer speech data collection can be used.

On the other hand, if the project is confidential and the data can not be shared with the public, then in-house data collection can be more suitable.

Budget of the project

As mentioned before, in-house data collection can be expensive and time-consuming; therefore, if the project has time and budget constraints, then working with prepackaged datasets or crowdsourcing data collection service providers can be more suitable.

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

Feel free to download our whitepaper for more in-depth knowledge on data collection/harvesting:

Get Data Collection Whitepaper

Further reading

If you need help finding a vendor or have any questions, feel free to contact us:

Find the Right Vendors

References:

  1. Song, Z. (2020). English speech recognition based on deep learning with multiple features. Computing, 102(3), 663-682.
  2. Marvin, Rob (May 2020). “Are You Worried About Smart Home Devices Listening to You?”. https://www.pcmag.com/news/are-you-worried-about-smart-home-devices-listening-to-you Retrieved: 09 Nov 2022
  3. Reed, L. (2004). The Requirements and Applications of Speech Recognition Technology for Voice Activated Command and Control in the Tactical Military Environment.Army Communications-Electronics Command Fort Monmouth NJ.
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple. He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers. He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations. He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.

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