Speech recognition, also known as automatic speech recognition (ASR), is a field of machine learning that involves the development of algorithms and models that can analyse and interpret spoken language. The goal of speech recognition is to enable computers to recognise and transcribe spoken words and phrases accurately.
The process of speech recognition typically involves the following steps:
Audio input: The first step in speech recognition is to collect audio input, typically through a microphone or other recording device.
Preprocessing: Once the audio input has been collected, it needs to be preprocessed to remove noise and normalize the audio levels.
Feature extraction: The next step is to extract features from the audio signal, such as spectral coefficients or Mel-frequency cepstral coefficients (MFCCs), which are used to represent the sound characteristics of the speech.
Acoustic modeling: The extracted features are then used to train an acoustic model, typically a deep neural network, to recognize and transcribe speech.
Language modeling: The transcribed speech is then processed by a language model, which uses statistical methods to predict the likelihood of different word sequences.
Decoding: The final step is to use a decoding algorithm, such as the Viterbi algorithm, to determine the most likely transcription of the spoken words.
Speech recognition has a wide range of applications, including virtual assistants, dictation software, and voice-activated devices. Some popular speech recognition frameworks and libraries include Google Cloud Speech-to-Text, Kaldi, and Mozilla DeepSpeech.