Application of Hidden Markov Models in Speech Command Recognition

Abstract

In this study, vector quantization and hidden Markov models were used to achieve speech command recognition. Pre-emphasis, a hamming window, and Mel-frequency cepstral coefficients were first adopted to obtain feature values. Subsequently, vector quantization and HMMs (hidden Markov models) were employed to achieve speech command recognition. The recorded speech length was three Chinese characters, which were used to test the method. Five phrases pronounced mixing various human voices were recorded and used to test the models. The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.