Updated: Jan 10
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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.
TOP 10 Speech-to-Text API:
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.