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The paper describes a coding scheme for broadband speech (sampling frequency 16 KHz). The authors present a wideband speech encoder called APVQ (adaptive predictive vector quantization). It combines subband coding, vector quantization and adaptive prediction. The speech signal is split into 16 subbands by means of a QMF filter bank and so every subband is 500 Hz wide. This APVQ encoder can be seen as a vectorial extension of a conventional ADPCM encoder. In this scheme, signal vector is formed with one sample of the normalized prediction error signal coming from different subbands and then it is vector quantized. The prediction error signal is normalized by its gain and normalized prediction error signal is the input of the VQ and therefore an adaptive gain-shape VQ is considered. This APVQ encoder combines the advantages of scalar prediction and those of vector quantization. They evaluate wideband speech coding in the range from 1.5 to 2 bits/sample, that leads to a coding rate from 24 to 32 kbps., Peer Reviewed, Postprint (published version)

We study some speech enhancement algorithms based on the iterative Wiener filtering method due to Lim-Oppenheim (1978), where the AR spectral estimation of the speech is carried out using a second-order analysis. But in our algorithms we consider an AR estimation by means of cumulant analysis. This work extends some preceding papers due to the authors, providing a generalization of third- and fourth-order algorithms by means of adding two parameters in the general expression of Wiener filtering. These parameters allow a better control of their performance. Some results are presented considering AWGN but listening tests give similar performance when other noises (diesel engine) are considered., Peer Reviewed, Postprint (published version)

Peer Reviewed, Postprint (published version)

The authors describe the acoustic processor of a Spanish continuous speech recognition system based on demisyllable units. The acoustic processor is based on a spotting algorithm which takes as input the unknown utterance, the HMM (hidden Markov model) of the reference demisyllables, and the lexical knowledge in terms of a finite state network. The spotting algorithm is a modified version of the one-step Viterbi algorithm with multiple hypotheses. The output of the system is a lattice of word hypotheses suitable to be parsed by a linguistic analyzer. The proposed acoustic processor was tested using the integers from 0 to 1000 and telephonic numbers in a speaker-independent approach. The results show the good performance of the demisyllable as a recognition unit for the Spanish language and the efficiency of the spotting algorithm., Peer Reviewed, Postprint (published version)