Experimental results on speaker-independent male and female speech show that accurate voicing classification and fundamental frequency prediction is attained when compared to hand-corrected reference fundamental frequency measurements. The second scheme uses a set of hidden Markov models (HMMs) to link together a set of state-dependent GMMs, which enables a more localized modeling of the joint density of MFCCs and fundamental frequency. The first method enables fundamental frequency prediction by modeling the joint density of MFCCs and fundamental frequency using a single Gaussian mixture model (GMM). In this work the voicing classification and fundamental frequency are predicted from the MFCC vectors themselves using two maximum a posteriori (MAP) methods. Previous methods for speech reconstruction have required, in addition to the MFCC vectors, fundamental frequency and voicing components. This work proposes a method to reconstruct an acoustic speech signal solely from a stream of mel-frequency cepstral coefficients (MFCCs) as may be encountered in a distributed speech recognition (DSR) system. Predicting fundamental frequency from mel-frequency cepstral coefficients to enable speech reconstruction.

Experiment results of testing samples from 3 subjects show that acceptable performances of cough recognition are obtained by DTW with a small training set. The process of cough recognition based on mel frequency cepstral coefficients (MFCC) and DTW is introduced. In this paper, according to the advantage of dynamic time warping (DTW) and the characteristic of cough recognition, an attempt is made to adapt DTW as the recognition algorithm for cough recognition. Cough Recognition Based on Mel Frequency Cepstral Coefficients and Dynamic Time WarpingĬough recognition provides important clinical information for the treatment of many respiratory diseases, but the assessment of cough frequency over a long period of time remains unsatisfied for either clinical or research purpose.

Cepstral voices 6.2 serials