Generalized mel frequency cepstral coefficients for large-vocabulary speaker-independent continuous-speech recognition

The focus of a continuous speech recognition process is to match an input signal with a set of words or sentences according to some optimality criteria. The first step of this process is parameterization, whose major task is data reduction by converting the input signal into parameters while preserving virtually all of the speech signal information dealing with the text message. This contribution presents a detailed analysis of a widely used set of parameters, the mel frequency cepstral coefficients (MFCC’s), and suggests a new parameterization approach taking into account the whole energy zone in the spectrum. Results obtained with the proposed new coefficients give a confidence interval about their use in a large-vocabulary speaker-independent continuous-speech recognition system.