Audio Input and Preprocessing: The process begins with the capture of audio signals, typically through a microphone. The audio signal is then digitized and subjected to preprocessing, where noise reduction, echo cancellation, and other enhancements are applied to improve the quality of the input.

Feature Extraction: Once the audio signal is preprocessed, it undergoes feature extraction. This step involves converting the raw audio waveform into a set of features that can be used by the recognition model. Common features include Mel-Frequency Cepstral Coefficients (MFCCs), which represent the short-term power spectrum of the sound, and spectrograms, which visualize the frequency content of the audio over time.

Acoustic Modeling: Acoustic models are responsible for mapping the extracted features to phonetic units, the basic sounds of speech. These models are typically trained using large datasets of labeled speech, allowing the system to learn the probability of certain phonetic units occurring in a given context.

Language Modeling: Language models play a crucial role in ASR by predicting the likelihood of word sequences. These models use statistical methods or neural networks to analyze patterns in language and improve the accuracy of word recognition. For example, in the phrase “I want to buy a car,” the language model helps the ASR system distinguish between homophones like “car” and “kar.”

Decoding and Post-Processing: The final step involves decoding, where the system combines the outputs of the acoustic and language models to generate the most likely transcription of the spoken input. Post-processing techniques, such as punctuation restoration and capitalization, are then applied to produce a readable text output.