The basic process of speech recognition consists of the following steps:

Signal sampling: A voice signal is a continuous analog signal that needs to be converted into a digital signal before it can be processed. The process of sampling is to collect the voice signal according to a certain time interval, and then convert the collected signal into a digital signal. Typically, the sampling frequency is 8kHz or 16kHz.

Feature extraction: The speech signal is converted from the time domain to the frequency domain, represented as a series of parameters containing energy and frequency information. The extracted features can represent the speech signal more effectively and provide the basis for subsequent recognition.

Acoustic modeling: The extracted speech features are matched to the acoustic model in the speech recognition system. Acoustic models describe the relationship between speech signals and specific pronunciations and are key to achieving speech-to-text conversion.

Language model: Modeling the results of recognition and speech recognition according to context to improve the accuracy of speech recognition. Language models describe probabilistic relationships between words and help determine the most likely recognition outcomes.

Decoder: The results of the previous steps are jointly decoded to generate the final recognition result. The decoder selects the most likely sequence of words as output based on the information from the acoustic model and the language model.

The basic flow of speech recognition includes signal sampling, feature extraction, acoustic modeling, language model and decoder. These steps are interrelated and together achieve the goal of converting speech signals into text.