Building Speech Models - Hidden Markov Models

Speech modeling is achieved through the implementation of Hidden Markov Models (HMMs), which are statistical models. The primary objective of speech modeling is to represent and analyze speech signals for applications in speech recognition and related fields. HMMs are widely used statistical models that describe dynamic systems with hidden states and observable outputs. In implementation, HMMs typically involve three key algorithms: the Forward-Backward algorithm for calculating observation probabilities, the Viterbi algorithm for finding the most likely state sequence, and the Baum-Welch algorithm for parameter estimation and training. When building speech models, feature extraction techniques like MFCC (Mel-Frequency Cepstral Coefficients) are commonly used to convert raw speech signals into observable sequences. The model structure often employs left-to-right architectures where states represent phonetic units, with Gaussian Mixture Models (GMMs) frequently used to model state emissions. Through such speech modeling implementations, we can better understand and process speech signals, enabling applications across various speech-related domains including speech recognition, speaker identification, and speech synthesis.