How Data Annotation is used for Speech Recognition
Speech recognition refers to a computer interpreting the words spoken by a person and converting them to a format that is understandable by a machine. Depending on the end goal, it is then converted to text or voice, or another required format. For instance, Apple's Siri and Google's Alexa use AI-powered speech recognition to provide voice or text support whereas voice-to-text applications like Google Dictate transcribe your dictated words to text.
Speech recognition AI applications have seen significant growth in numbers in recent times as businesses are increasingly adopting digital assistants and automated support to streamline their services. Voice assistants, smart home devices, search engines, etc are a few examples where speech recognition has seen prominence.
Data is required to train a speech recognition model because it allows the model to learn the relationship between the audio recordings and the transcriptions of the spoken words. By training on a large dataset of audio recordings and corresponding transcriptions, the model can learn to recognize patterns in the audio that correspond to different words and phonemes (speech sounds).
For example, if the model is trained on a large dataset of audio recordings of people speaking English, it will learn to recognize common patterns in the audio that corresponds to English words and phonemes. These patterns might include the frequency spectrum of different phonemes, the duration of different vowel and consonant sounds, and the context in which different words are used. By learning these patterns, the model can then take as input a new audio recording and use what it has learned to transcribe the spoken words in the audio. Without a large and diverse dataset of audio recordings and transcriptions, the model would not have enough data to learn these patterns and would not be able to perform speech recognition accuracy.
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