Speech Production Model

Abstract

AbstractThe continuous speech signal (air) that comes out of the mouth and the nose is converted into the electrical signal using the microphone. The electrical speech signal thus obtained is sampled to obtain the discrete signals and are stored in the digital system for further processing. This is digital speech processing. The speech signal model is broadly classified as the source-filter model and the probabilistic model. Source-filter model assumes the physical phenomenon for the production of speech signal. Probabilistic model like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM) are the mathematical model that does not care about the physical phenomenon. Speech model is used to extract the feature vectors from the speech signal for isolated speech recognition and the speaker recognition. It is used to compress the speech signal for storage like in Code exited linear prediction (CELP). It is useful for converting text into speech, known as speech synthesis. It is also used for continuous speech recognition. This chapter deals with the source-filter model of speech production.KeywordsSpeech SignalSound WaveVocal TractOrder FilterAutocorrelation MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.