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In this article, we are going to see how we can easily integrate a Speech recognition engine in your project and how to choose and access the right engine according to your data.
Speech recognition technology allows you to turn any audio content into written text. It is also called automatic speech recognition, or computer speech recognition. Speech recognition 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.
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.
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
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.
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.
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.
Rev.ai's suite of speech-to-text APIs allows businesses to build powerful downstream applications. They train their speech engine on 50,000+ hours of human-transcribed content from a wide range of topics, industries, and accents.
Amberscript is building SaaS solutions that enable users to automatically transform audio and video into text and subtitles using speech recognition. They use the data their users generate to train the best speech recognition engines in European languages. Their online text editor and human transcribers bring the text to 100% accuracy.
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.
You can use Speech Recognition in numerous fields, and sometimes specific models are trained for those fields. Here are some common use cases:
When you need a Speech Recognition engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Speech recognition. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Speech-to-Text engines with a provider as a simple parameter. With only few lines, you can set up your project in production:
Test and API:
Here is the code in Python (GitHub repo) that allows to test Eden AI for speech-to-text:
Eden AI also allows you to compare these engines directly on the web interface without having to code:
There are numerous Speech engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Speech recognition technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
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