-p-500.webp.svg)
Start Your AI Journey Today
- Access 100+ AI APIs in a single platform.
- Compare and deploy AI models effortlessly.
- Pay-as-you-go with no upfront fees.
Best Speech-to-Text (STT) / Automatic Speech Recognition (ASR) APIs in 2025
Speech-to-Text (STT) technology allows you to turn any audio content into written text. It is also called Automatic Speech Recognition (ASR), or computer speech recognition. Speech-to-Text is based on acoustic modeling and language modeling.
Note that it is commonly confused with voice recognition, but it focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.
You can use Speech Recognition in numerous fields, and some STT APIs are built especially for those fields. Here are some common use cases:
.jpeg)
Speech experts at Eden AI tested, compared and used many Speech-to-Text APIs of the market. There are many actors and here are actors that perform well (in alphabetical order):
AssemblyAI’s Speech-to-Text API provides highly accurate transcription services for audio and video files, live speech, and more. It features advanced capabilities like speaker detection, sentiment analysis, PII redaction, and speech summarization. The API integrates easily with Python, Node.js, Java, and REST APIs, offering scalability with competitive pricing.
AssemblyAI uses cutting-edge deep learning models like Conformer-2 for transcription accuracy and supports real-time processing for various use cases such as call center automation, media analytics, and meeting transcription. It also offers 24/7 customer support and integrations with cloud storage platforms like S3, GCS, and Azure.
Amazon Transcribe’s API offers real-time and batch speech-to-text transcription in over 100 languages. It features automatic punctuation, speaker diarization, custom vocabulary, language detection, and content redaction. The API helps businesses extract insights like sentiment analysis and call categorization, particularly with Amazon Transcribe Call Analytics. It delivers accurate transcriptions even in noisy environments, making it ideal for customer service, media, and more, with easy integration into AWS services.
DeepAI’s Speech-to-Text API offers advanced speech recognition with a focus on accuracy, speed, and cost-effectiveness. It provides several model options, including Nova and Whisper, which deliver improved performance over other services in terms of accuracy, processing speed, and cost.
The API supports real-time transcription with low latency (under 300ms) and is capable of handling multiple languages and dialects. It also allows for custom models tailored to specific needs, improving transcription accuracy, especially for specialized vocabulary. This solution is designed to meet both enterprise and startup requirements with scalability and flexibility.
Gladia’s Speech-to-Text API delivers accurate real-time transcription with advanced features like speaker diarization, word-level timestamps, and entity recognition. Supporting 100+ languages and code-switching, it ensures precise transcription across multilingual and technical conversations. Optimized for enterprise use, it is easy to integrate, secure, and compliant, making it ideal for applications in AI assistants and contact centers.
Google Cloud Speech-to-Text API supports transcription in 125+ languages with high accuracy. It offers pretrained or customizable models for various use cases, including voice control, calls, and videos. The API supports short, long, and streaming audio, with options for synchronous, asynchronous, or real-time transcription. It also ensures enterprise-level security and compliance, with data residency, customer-managed encryption, and model adaptation to improve accuracy for specific terms.
IBM Watson Speech to Text API offers fast, accurate transcription in multiple languages for various use cases, including self-service and speech analytics. It features real-time transcription, speaker diarization, keyword spotting, and smart formatting. The API is customizable for specific domains and acoustic characteristics and ensures robust security with deployment flexibility across cloud or on-premises environments. With both pre-trained and customizable models, it adapts to diverse business needs.
Microsoft Azure Speech to Text API offers real-time and batch transcription for over 85 languages, with features like speaker diarization and customizable models for improved accuracy in specific domains. It supports various use cases such as live captions, customer service, healthcare documentation, and video subtitling. The service can be integrated via SDK, CLI, or REST API, and provides options to adjust transcription for domain-specific vocabulary and audio conditions. It also allows efficient processing of large audio files and provides real-time results for immediate transcription needs.
OpenAI's Speech-to-Text API, powered by the Whisper model, offers advanced transcription and translation capabilities for 99 languages. It handles various accents and background noise, providing two endpoints: transcription (audio to text) and translation (non-English to English). Using a transformer-based architecture, Whisper processes audio in 30-second chunks and generates text from log-Mel spectrograms, making it ideal for real-time captioning and multilingual content creation.
Rev.ai provides highly accurate speech-to-text services with both machine and human-generated transcription. It supports asynchronous and real-time streaming transcription in 58+ languages, with advanced NLP features like language identification, sentiment analysis, and summarization. Known for its low word error rate, it offers flexible deployment, robust security (SOC II, HIPAA, GDPR), and easy integration with SDKs. It’s ideal for industries like media, healthcare, and customer service.
Sightengine's Image Moderation API uses AI to detect harmful content like nudity, violence, drugs, and weapons in images, videos, and live streams. It supports large-scale processing, customizable settings, and easy integration via REST APIs and SDKs. Ideal for social media, e-commerce, and content platforms, it ensures privacy compliance and real-time moderation for safe, scalable content.
Speechmatics provides highly accurate, mission-critical speech recognition for industries like contact centers, CRM, security, and media. Supporting over 30 languages, it processes millions of transcription hours monthly, offering real-time and batch transcription, speaker diarization, and custom dictionaries. With flexible deployment options (cloud, on-prem, or on-device), Speechmatics ensures reliability, high accuracy, and reduced AI bias, even in challenging environments and diverse dialects.
Symbl.ai offers advanced speech-to-text transcription for real-time and asynchronous use cases, supporting over 20 languages and dialects. It features high accuracy with speaker separation, customizable vocabulary, and multi-streaming connections. Symbl.ai enables real-time captioning, searchable conversation archives, and conversation insights for applications like video calls, webinars, and customer service. Transcripts can be exported in formats like SRT or markdown for easy integration.
Medallia Speech offers a real-time, AI-powered speech-to-text API with high accuracy and low latency. It handles large audio files, multiple languages, and accents, providing features like speaker diarization, keyword spotting, and text analytics. Used in call centers, transcription services, and voice-enabled devices, it captures metrics such as time, emotion, and gender to generate actionable insights, improving customer experience and contact center performance. The solution integrates easily through APIs in Medallia's Experience Cloud platform.
For all the companies who use voice technology in their softwares and for their customers, cost and performances are real concerns. The voice market is dense and all those providers have their benefits and weaknesses.
Performance variations according to the languages
Speech-to-Text APIs perfom differently depending the language of audio. In fact, some providers are specialized in specific languages. Different specificities exist:
Performance variations according to audio data quality
When testing multiple speech-to-text APIs, you will find that providers accuracy can be different according to audio format and quality. Format .wav, .mp3, .m4a will impact performance as well as the sample rate that can be most of the time 8000Hz, 16 000Hz and higher. Some providers will perform better with low quality data, other with high quality.
Performance variations according fields
Some STT APIs trained their engine with specific data. This means that speech-to-text APIs will perform better for audio in medical field, other in automotive field, other in generic fields, etc. If you have customers coming from different fields, you must consider this detail and optimize your choice.
All the companies that have speech recognition feature in their product or deal with voice technology for their customers have to use multiple speech-to-text APIs. This is mandatory to reach high performance, optimize cost and cover all the customers needs. There are many reasons for using multiple APIs:
Eden AI has been made for multiple speech-to-text APIs use. Eden AI is the future of speech recognition usage in companies. The Eden AI API speech-to-text APIs allows you to call multiple speech-to-text APIs and handle all your voice issues:
The Eden AI team can help you with your speech recognition integration project. This can be done by :
You can directly start building now. If you have any questions, feel free to chat with us!
Get startedContact salesDrop your email and we'll get back to you ASAP to answer any questions you have or just to say hi —we promise not to spam you!
-p-500.webp){kind=small}
-p-500.webp){kind=small}
.jpg)
.jpg)
.jpg)