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A correct choice of voice signal modeling methods is essential to obtain good results in automatic speech recognition. In this paper, we have proposed a unified view of the speech parametrization stage, in which conventional techniques as Linear Prediction Coeficientes and mel-cepstrum filter bank are viewed as particular cases. The model incorporates a new technique of deconvolution, that is called root homomorphic deconvolution. A broad set of experimental results are also presented

The performance of existing speech recognition systems degrades rapidly in the presence of background noise when training and testing cannot be done under the same ambient conditions. The aim of this paper is to report the application of several robust techniques on a system based on the HMM (Hidden Markov Models) and VQ (Vector Quatization) approaches for speech recognition in car environment optimization of spectral model order and cepstral lifter, parameterizatrion based on the AR modeling of the autocorrelation sequence -proposed by the authors in [1]-, use of dynamic information and multilabeling.

Cepstral coefficients obtained by means of a linear predictive analysis (LPC) of either the signal or a related sequence have given remarkable robustness against additive Gaussian white noise. From a comparative study of the most robust techniques appeared in the literature and some shight alternatives proposed in this paper, we attain two main conclusions : 1) the slope cepstral window and a relatively high model order are preferable; and 2) the spectral representation based on the autocorrelation (rather than on the signal) modelling achieves better results than the high performance cepstral projection distance measure for low signal-to-noise ratios.

Cepstral coefficients are widely used in speech recognition. In this paper, we claim that they are not the best way of representing the spectral envelope, at least for some usual speech recognition systems. In fact, cepstrum has several disadvantages: poor physical meaning, need of transformation, and low capacity of adaptation to some recognition systems. In this paper, we propose a new representation that significantly outperforms both mel-cepstrum and LPC-cepstrum techniques in both recognition rate and computational cost. It consists of filtering the frequency sequence of filter-bank energies with an extremely simple filter that equalizes the variance of the cepstral coefficients. Excellent results of the new technique using a continuous observation density HMM recognition system and two very different recognition tasks, connected digits and phone recognition, are presented.

La recerca i el desenvolupament d'aplicacions en tecnologies de la parla han deixat força de banda els coneixements lingüístics. Les raons són diverses, com veurem, però la dificultat del treball conjunt de lingüistes i tecnòlegs no significa que aquest no sigui necessari per arribar més lluny en els objectius de la pròpia tecnologia.

Recently, the set of spectral parameters of every speech frame that result from filtering the frequency sequence of mel-scaled filter-bank energies with a simple first-order high-pass FIR filter have proved to be an efficient speech representation in terms of both speech recognition rate and computational load. In this paper, we apply the same technique to speaker recognition. Frequency filtering approximately equalizes the cepstrum variance, enhancing the oscillations of the spectral envelope curve that are most effective for discriminating between speakers. In this way, even better speaker identification results than using conventional mel-cepstrum were observed in continuous observation Gaussian density HMM, especially in noisy conditions.

SIMBAD has been developed to facilitate the tasks involved in the design of a concatenative speech synthesis system: 1) building a dictionary of parameterized speech units, and 2) obtaining a set of rules to concatenate these units and to model prosodics. It is an interactive, menu-driven and graphical software tool that allows both automatic processing and graphical editing of speech with high flexibility.