Speach to Text

Audio Input: Speech-to-text systems take in audio recordings containing human speech as input. This audio input can be captured through microphones, telephones, or other audio recording devices. Preprocessing: Before speech recognition can occur, the audio input is typically preprocessed to enhance its quality and remove noise. Preprocessing techniques may include filtering, noise reduction, and normalization to ensure accurate transcription.
Feature Extraction: The preprocessed audio signal is then transformed into a sequence of feature vectors that represent acoustic properties such as frequency, amplitude, and duration. Common techniques for feature extraction include Mel-frequency cepstral coefficients (MFCCs), spectrograms, and linear predictive coding (LPC).
Acoustic Modeling: In this step, the speech signal is matched against acoustic models, which represent the statistical relationship between the extracted features and phonemes (the basic units of sound in a language). Acoustic models can be based on Hidden Markov Models (HMMs), deep neural networks (DNNs), or hybrid approaches.
Language Modeling: Once phonemes are identified, language models are used to determine the most likely sequence of words that match the phonetic transcription. Language models capture the syntactic and semantic structure of language and help to disambiguate between words with similar acoustic representations.
Decoding: The recognized phonetic sequence is then decoded into words using language models. This process involves selecting the most probable word sequence based on the acoustic and language model probabilities.
Output Text: Finally, the recognized words are converted into written text, producing the transcribed output of the spoken input