Audio Data Collection for AI: Challenges & Best Practices in 2024

As the global market for voice recognition and virtual assistants rises (See Figure 1), so does the demand for audio data collection services. And the more sophisticated these systems become, the larger their datasets should be.

Figure 1. Voice recognition market size worldwide from 2020 to 2029 (in billion U.S. dollars)¹

Due to data collection ethical constraints and other aspects, which we will discuss later in the article, collecting large and high-quality audio datasets can be challenging.

In this article, we explain what audio data collection is, what challenges one might face while collecting audio data, and some best practices.

You can also work with an audio or speech data collection service to acquire relevant training data for your machine-learning models.

What does audio data collection mean for AI/ML?

Audio data collection/harvesting/sourcing means gathering audio data to train and improve an AI/ML model. Such data includes:

• Speech data (Spoken words by humans in different languages, accents, and dialects)
• Different sounds (Animal sounds, sounds of objects, etc.)
• Music data (music or song recordings)
• Other digitally recorded human sounds, such as coughs, sneezes, or snores 
• Far-flung speech or other background noises

All this audio data can be used to train the following technologies:

• Virtual assistants in smartphones,
• Smart home devices and appliances (Google Home, Siri, Alexa, etc.)
• Smart car systems
• Voice recognition systems for security
• Voice bots
• Other voice-enabled solutions

Example of ChatGPT

ChatGPT introduced voice input and output features that can accurately process voice inputs from users and provide realistic voice outputs. This is possible through gathering human-generated audio data.²

What are the challenges in collecting audio data?

1. Language and accent challenge

There is a rising global demand for smart home devices (See Figure 2). And to extensively deploy such devices, you need audio data in different languages and accents. Acquiring it takes time. 

For instance, Amazon Echo is available in more languages and countries than Google Home since they have been in the market for a longer period of time. Even though Google Home was launched 6 years ago, it has only been launched in around 11 countries. One of the reasons for this has been the difficulty in expanding datasets with different languages and accents.

Figure 2. The smart home devices market is expected to boom

2. Time-consuming

As compared to image data, recording audio data consumes more time. This is because audio data is recorded in real-time and can not be captured at a single point in time like an image. 

Audio data collection can be more time-consuming if the data is being gathered:

• in different languages and accents
• of different types of voices (female, male, high/low pitched, etc.)
• is of different resolutions and formats
• includes jargon or variations in the voice (such as emotions)

For example, in a recent study on developing voice-based human identity recognition, collecting audio data from only 150 participants from a single region took more than 2 months.

3. Costly

In-house audio data collection can be expensive and labor-intensive, depending on the project’s scope. If off-the-shelf datasets are insufficient for your project, then audio data collection can significantly add to the project budget. 

Studies show that there is a positive correlation between the size of the data and the accuracy of the model being trained. Consequently, the larger the dataset, the higher the cost of collection. 

Some factors that can impact the cost of audio data collection include

• Recruitment of contributors and collectors
• Voice recording and storage equipment

4. Ethical and legal challenges

Another challenge in gathering audio data, specifically speech data, is people’s unwillingness to share it. Due to reasons such as privacy and security, many people are hesitant to share their voice data since it is a type of biometric data. 

To learn more, check out these comprehensive articles on:

• Ethical & Legal Data Collection for AI Training with Case Studies
• Top 4 Speech Recognition Challenges and Solutions

What are some best practices for audio data collection?

To overcome the aforementioned challenges, the following best practices can be considered:

1. Leverage outsourcing or crowdsourcing

Audio data collection processes can be outsourced or crowdsourced depending on the size and scope of the project. If the dataset needs to be simple and small/medium, outsourcing can be the way to go. On the other hand, large and diverse datasets can be collected through crowdsourcing.

For instance, data collection service providers working with a crowdsourcing model will use microtasks that will reduce the cost of collecting the data and make it diverse. 

Through these methods, the company can also transfer the burden of ethical and legal considerations to the third-party service provider.

2. Automation

Another way of data collection is automation. You can program a bot to collect audio data through online sources. This can be done in-house and reduces the need for excess recruitment of contributors. However, in automated data collection, maintaining the quality of the data can be challenging since the data is collected in masses without any scrutiny.

3. Ethical and legal considerations

It is important to consider ethical and legal factors before collecting any type of data to avoid expensive lawsuits. As mentioned earlier, audio data is biometric data; therefore, the data collectors must ensure transparency. 

For instance, if a smart home device is collecting voice data of the user to train itself, it must inform the user and provide an option for the user to opt out.

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

FAQs for audio data collection

Further reading

• Top 6 Data Collection Best Practices
• 5 Step Roadmap to Data Collection Process for AI/ML Models

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

External resources

• 1. Voice recognition market size worldwide from 2020 to 2029 (in billion U.S. dollars). Statista. Accessed: 26/March/2024.
• 2. ChatGPT can now see, hear, and speak. OpenAI. Accessed: 26/March/2024.