Algorithms Of Warped Discrete Fourier Transform And Speech Signal Processings

Warped Discrete Fourier Transform (WDFT) has had more and more attention only recently. Frequency axis warping to achieve nonuniform Fast Fourier Transform (FFT) resolution was first introduced by A. V. Oppenheim and D. Johanson, using a network of cascaded first order allpass sections for frequency warping of the signal, followed by a standard FFT. This idea was later extended to Warped Linear Prediction (WLP) by H. W. Strube, and was ultimately applied in an ADPCM codec. Beyond the performance improvements realized only through the increase of sampling points, there has been big interest in warping the frequency axis to effectively provide better resolution at some frequencies than at others.Since WDFT offers another choice to increase frequency resolution at any selected parts of spectral axis without extending the number of sampling points N, as well as to determine a frequency sample at any exact point by choosing the allpass parameters, it may be used as a very powerful tool in Digital Signal Processing (DSP), especially in Speech Signal Processing (SSP). After the introduction of WDFT and the special characteristics in SSP, several new schemes for SSP are proposed. The concept of WDFT is then extended to two dimensions to serve as a basic framework for warped nonuniform sampling of 2-D sequences in frequency domain. So the Algorithms of WDFT and SSP are discussed systematically in this dissertation and the main achievements are listed as follows:1 . Perceptual Linear Prediction (PLP) plays an important role in speech recognition. A new extraction algorithm for PLP based on WDFT is suggested in this paper and the extracting scheme by new PLP algorithm based on WDFT is presented afterwards. The new algorithm uses WDFT to improve the low frequency resolution of the input speech and it is more consistent with the characteristics of human auditory system than conventional methods. Hence, this new algorithm has better speech recognition rate than the conventional PLP based on FFT. Further more, by selecting the smaller sampling number N of nonuniformly spaced sample points in the spectral domain in this new algorithm than in the conventional one, the same speech recognition rate is achieved with higher robust performance. It becomes an effective solution to recognize the easy-confused speeches with same vowel but different consonant.