Here is our selection of the best Speech-to-Text APIs to help you choose and access the right engine according to your data.
Speech-to-Text, also known as Automatic Speech Recognition (ASR) or Computer Speech Recognition, is a technology based on acoustic modeling and language modeling, that converts any audio content into written text. Note that it is often confused with speech recognition, but it focuses on translating speech from a verbal to a text format, whereas speech recognition simply seeks to identify the voice of an individual user.
This feature can be used to subtitle videos, transcribe phone calls or recordings.
In 1952, Bell Laboratories designed the first Speech Recognition which could recognize a single voice speaking digits aloud. Ten years later, IBM introduced “Shoebox” which understood and responded to 16 words in English.
In the early 1970s, the U.S. Department of Defense’s ARPA funded a five-year program which could recognize just over 1000 words by 1976.
A key turning point came with the popularization of Hidden Markov Models (HMMs) in the mid-1980s. HMM uses probability functions to determine the correct words to transcribe. The next big breakthrough came in the late 1980s with the addition of neural networks. This was also an inflection point for ASR.
Assembly AI allows to accurately transcribe audio and video files with a simple API. Their Speech-to-Text engine is powered by advanced AI models. Assembly AI offers: batch asynchronous transcription, real-time transcription, speaker diarization, all audio and video formats accepted, top-rated accuracy, automatic punctuation and casing, word timings, confidence scores, paragraph detection.
Amazon Transcribe makes it easy for developers to add speech to text capabilities to their applications. Amazon Transcribe uses a deep learning process called Automatic Speech Recognition (ASR) to convert speech to text quickly and accurately. Amazon Transcribe can be used to transcribe customer service calls, automate subtitling, and generate metadata for media assets to create a fully searchable archive.
Deepgram provides developers with the tools you need to easily add AI speech recognition to applications. We can handle practically any audio file format and deliver at lightning speed for the best voice experiences. Deepgram Automatic Speech Recognition helps you build voice applications with better, faster, more economical transcription at scale.
Speech-to-Text enables easy integration of Google speech recognition technologies into developer applications. Send audio and receive a text transcription from the Speech-to-Text API service.
IBM Watson Speech to Text technology enables fast and accurate speech transcription in multiple languages for a variety of use cases, including but not limited to customer self-service, agent assistance and speech analytics. They provide advanced machine learning models out-of-the-box or customize them for your use case.
Microsoft Azure Speech-to-Text service defaults to using the Universal language model. This model was trained using Microsoft-owned data and is deployed in the cloud. It's optimal for conversational and dictation scenarios. When using speech-to-text for recognition and transcription in a unique environment, you can create and train custom acoustic, language, and pronunciation models.
Speechmatics powers applications that require mission-critical, accurate speech recognition using its any-context speech recognition engine. Speechmatics’ speech recognition technology is used by enterprises in scenarios such as contact centers, CRM, consumer electronics, security, media & entertainment and software. Speechmatics processes millions of hours of transcription worldwide every month in 30+ languages.
Sonix provides accurate, automated transcription in 35+ languages including Spanish, French, German, Chinese, Hindi, Arabic, and many more. Sonix is an online transcription platform. Upload a file to Sonix, and you'll have an online transcript in less than 5 minutes. Auto speaker separation. Auto-punctuation. Browser-based transcript stitches audio/video to text. Multiple languages. Easily search & analyze all your transcripts for qualitative analysis and coding.
The Symbl API uses advanced machine learning techniques to transcribe speech in real-time and provide additional context-aware insights such as speaker identification, sentiment analysis, and topic detection.
Voci offers advanced and accurate transcription services for various use cases. Their API can transcribe speech in real-time, process large audio files, and handle multiple languages and accents. Voci's API uses deep neural networks to perform speech recognition, which allows for high accuracy and low latency. Additionally, Voci also provides text analytics, speaker diarization, and keyword spotting. Their API can be integrated into various applications such as call centers, transcription services, and voice-enabled devices.
Speech-to-Text technology has a wide range of applications and can be used in various fields. Here are some examples of how STT can be used in different fields :
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Speech-to-Text tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment analysis API, Summarization, Question Answering, Data Anonymization, Speech recognition, and so forth.
We want our users to have access to multiple Speech-to-Text engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs:
You need to set up a provider API that is requested if and only if the main Speech-to-Text API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
After the testing phase, you will be able to build a mapping of providers performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best Speech-to-Text API.
You can choose the cheapest Speech-to-Text provider that performs well for your data.
This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because Speech-to-Text APIs will validate and invalidate each other for each piece of data.
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
You can see Eden AI documentation here.
The Eden AI team can help you with your Speech-to-Text integration project. This can be done by :
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