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10+ Speech Data Collection Services in 2024

Updated on Mar 26
7 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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10+ Speech Data Collection Services in 202410+ Speech Data Collection Services in 2024

Speech data collection services are a cornerstone of modern AI development. Speech or voice data is particularly necessary for natural language processing (NLP) and automatic speech recognition (ASR) systems. As AI continues to advance, the demand for high-quality speech datasets has surged, prompting many companies to seek services that can provide diverse and multilingual audio data.

This article compares the top speech data collection services and platforms to help businesses and AI developers with their speech data needs.

Speech data collection services comparison

Selecting a service provider for collecting speech data is a significant decision for any AI project. The tables below offer the top companies in the market offering speech data collection and generation services:

Table 1. Comparison based on the market presence & experience criterion

PlatformsUser Ratings
Out of 5 (Avg)*
Number of
FoundedData Collection
Amazon Mechanical Turk4282005
Telus International4.3102005
Summa Linguae TechnologiesN/AN/A2011
Toloka AIN/AN/A2014
Innodata IncN/AN/A1988
DataForce by TransperfectN/AN/A1992

* The data was gathered from B2B review platforms such as G2, Trustradius, and Capterra.

** If the company mentions data collection as the first offering on its website, we consider it to be data collection-focused.

*** Based on vendor claims from the corporate website.

Table 2. Comparison based on the platform capabilities criterion

Languages***Mobile applicationAPI availabilityISO 27001 CertificationCode of Conduct
Telus International500+
Summa Linguae
Toloka AI40+
Innodata Inc40+
DataForce by


  • The comparison table is created through publicly available and verifiable data.
  • The tables are ranked based on the number of reviews
  • The vendors were selected based on the relevance of their services. This means that all vendors that offered speech or voice data collection or generation were included.
  • Apart from speech data, all companies cover a wide array of data types for their data collection & annotation services (image, video, text, etc.).
  • Another filter used to narrow down the vendors was 50+ employees.
  • This table will not be updated regularly therefore, you can check out our data-driven list of data collection services to find the right option for your speech data needs.
  • In table 2, a company is assumed to follow a code of conduct if it has a code of conduct page on its website.
  • Transparency statement: AIMultiple serves numerous emerging tech companies and vendors, including the ones linked in the tables.

Criteria for selecting a speech data collection service

This section covers the criteria you can use to narrow down speech data collection services to fit your data needs.

Market presence & experience

  1. User ratings: High average ratings on B2B platforms suggest strong customer satisfaction.
  2. Number of reviews: More reviews indicate a broad user base and offer insight into customer experiences.
  3. Founded: Consider the company’s founding year since older companies typically have more refined services due to their experience. However, this is not always the case, so combine this criterion with customer reviews.
  4. Data collection focused: If the company offers data collection and generation as its primary offering, it will have more expertise in it.

Platform capabilities

  1. Audio transcription: Having audio transcription as a side service can facilitate the process of preparing speech datasets.
  2. Audio annotation: Essential for preparing speech datasets that are ready for AI model training.
  3. Languages: It is necessary to check which languages are covered by the service provider and if the language(s) you require is available.
  4. Mobile application: Facilitates on-the-go project management and unique voice data collection scenarios.
  5. API integration: Enables efficient data transfer and processing.
  6. ISO certification: Indicates adherence to global standards for data security and quality.
  7. Code of conduct: Reflects commitment to ethical practices towards the workforce.
  8. Crowd size: A large, diverse global workforce enhances scalability and solution diversity. A bigger crowd can offer speech datasets in more languages and dialects:

Figure 1. Comparison of the crowd size of all the companies compared in this article


  • In Figure 1, Innodata Inc. and TaskUS were not included since their crowd size was less than 100K.
  • For Figure 1, some vendors were also not included since their crowd size data was not found.

Company evaluation

Here’s a brief overview of the companies listed earlier in the tables

1. Clickworker

Clickworker specializes in AI data collection and generation through a crowdsourcing platform, covering multiple data types, including speech, audio, image, video, text, etc.


  • Human-generated speech datasets in multiple languages
  • Image and Video data collection services
  • Human-generated and collected datasets
  • Data annotation services
  • Audio transcription and translation services

Clickworker’s pros and cons

  • Customers consider the company’s crowd reliable and the platform to be user-friendly.1
One of the speech data collection services Clickworker's positive review on reliability and ease-of-use from G2.
  • Customers find its annotation services useful and effective.2
Clickworker's positive review on image data annotation from G2 for the image data collection article.

2. Appen

Appen works with a crowdsourcing platform focusing on deep learning, image data, and machine-learning models.


  • Image and video datasets
  • Audio and text data collection services
  • Annotation services for visual and audio data
  • Scalable solutions for diverse AI needs

Appen’s pros and cons:

  • Appen’s performance is declining, according to news of it losing clients and going through financial losses.3
  • Customers also identified server crashes on Appen’s platform.4
One of the speech data collection services, Appen's negative review from G2.

3. Prolific

Prolific also offers human-generated datasets through a crowdsourcing platform.


  • Data collection
  • Image annotation
  • Handwriting analysis
  • Research data for academia

Prolific’s pros and cons:

  • One of the drawbacks identified by analyzing the review is that most of the reviews are regarding its research-related services, which indicates that Prolific’s AI services may not be that popular.5
  • Even though some research customers found Prolific’s customer support to be good, they had issues with the platform’s inability to set customized quotas based on geographic and demographic parameters.6
Prolific's positive and negative reviews for its speech data collection services from G2.

4. Innodata Inc

Specializing in creating AI training data, Innodata Inc. offers speech, image, text, and audio data solutions to train machine learning models.


  • Scalable audio collection service
  • Machine learning project consultancy
  • Data security solutions

5. Telus International

Telus International offers AI solutions that span across machine learning, computer vision, and natural language processing.


  • Scalable speech and audio datasets
  • Object recognition solutions
  • Other data services for AI development

6. DataForce by Transperfect

DataForce caters to specific AI development needs, offering a blend of speech, image, video, and audio data.


  • Audio and voice datasets
  • Image and video data collection services
  • Experienced project managers for AI needs

7. Amazon Mechanical Turk

Amazon Mechanical Turk, or MTurk, offers crowd-sourced data collection and diverse data solutions ranging from speech to audio.


  • Large-volume data collection
  • Annotation services for various data types
  • Integration with the vast Amazon ecosystem

MTurk’s pros and cons:

  • Customers found its service quick, but the quality of the data provided by the workers was low.7.
Negative review of Amazon mechanical turk regarding the low quality of its speech data collection services from G2.

8. Summa Linguae Technologies

With a focus on providing custom solutions, Summa Linguae offers tools and services that cater to unique AI project requirements.


  • Custom and segmented data collection
  • Machine learning model training data
  • Data security and quality assurance

9. Toloka AI

Working with a crowdsourcing platform, Toloka AI specializes in collecting data for AI models, especially natural language processing (NLP).


  • Scalable speech and voice data solutions
  • Image and video data collection
  • Annotation services for various data types
  • Tools for specific AI program needs

10. LXT

LXT is an emerging player in the data collection domain, specializing in curating datasets tailored for AI and machine learning models.


  • Speech and voice data collection for NLP
  • Image and video data collection for machine learning models
  • Annotation services with an emphasis on accuracy
  • Custom dataset creation for unique AI project

11. TaskUS

TaskUS offers data types, including speech, audio, image, and video, for AI and machine learning models. However, their key offering is in the customer experience domain.


  • Speech datasets in multiple languages
  • Scalable image and video data solutions
  • Annotation services for various data types
  • Tools for specific AI program needs

Final recommendations

As artificial intelligence, machine learning algorithms, and speech recognition systems become more integral to our daily lives, the demand for comprehensive speech data collection services is only expected to grow. 

Speech data collection services are essential for acquiring the necessary data that train AI to understand and process human language effectively. By choosing a speech data partner that meets the criteria outlined above, companies can ensure they receive high-quality data that is ethically sourced and accurately annotated, laying a strong foundation for their data science projects.

Pay attention to these aspects while choosing your data partner:

  • Level of diversity: It is important to work with a partner that offers a large and diverse workforce
  • Customer satisfaction: You can analyze reviews and customer references and assess whether the customer can meet deadlines. 
  • Clear description and understanding: Clarify edge cases so the workforce can work efficiently without needing to pause and ask for clarification.

Speech data collection services FAQs

What is speech data collection?

Speech data collection is the process of gathering and recording human speech to create datasets that are essential for training and improving various technologies like natural language processing (NLP), automatic speech recognition (ASR) systems, and voice-enabled applications. These collections are often carried out by specialized speech data collection services, which ensure the capture of high-quality audio data from native speakers in multiple languages.

This diversity is crucial for developing speech recognition models that can understand and interpret the nuances of different speech patterns, accents, and dialects, thereby serving a diverse and multilingual audience.
The collected speech and audio data, once processed and annotated, become valuable audio datasets that serve as audio training data for machine learning models. These models are at the heart of a wide range of applications, from virtual assistants and voice recognition technologies to more complex conversational AI systems. Ensuring data quality, such as clear recordings with minimal background noise and a wide variety of spoken language scenarios, is key to training machine learning algorithms effectively.

Moreover, the scale and scope of speech data collection projects can vary, encompassing everything from simple voice commands to more complex conversational interactions in various environments. This versatility supports the development of AI models capable of semantic analysis and understanding human speech in a natural and intuitive manner, thereby enhancing the accuracy and efficiency of voice-enabled technology across different platforms and devices.

How to find the best speech data collection service?

Enhancing your speech generation tool’s effectiveness hinges on the quality of speech data provided. Opting for a service provider with a strong market presence and a broad, diverse contributor network is crucial, especially one that offers high-quality audio data in multiple languages. Ideal providers should specialize in comprehensive speech and audio data collection services, including transcription and annotation, to enrich your audio datasets. This is vital for developing advanced speech recognition systems and machine learning models attuned to diverse, multilingual audiences. Such strategic partnerships can significantly elevate the performance of voice-enabled applications and conversational AI, ensuring they effectively understand and process human speech across various environments.

Further reading

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

Find the Right Vendors

External resources

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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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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