Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.

The SIFT algorithm for fundamental frequency estimation

Abstract: In this paper a new method for estimating F 0 , the fundamental frequency of voiced speech versus time, is presented. The algorithm is based upon a simplified version of a general technique for fundamental frequency extraction using digital inverse filtering. It is demonstrated that the simplified inverse filter tracking algorithm (hereafter referred to as the SIFT algorithm) encompasses the desirable properties of both autocorrelation and cepstral pitch analysis techniques. In addition, the SIFT algorithm is composed of only a relatively small number of elementary arithmetic operations. In machine language, SIFT should run in several times real time while with special-purpose hardware it could easily be realized in real time.

From frequency to quefrency: a history of the cepstrum

Abstract: The idea of the log spectrum or cepstral averaging has been useful in many applications such as audio processing, speech processing, speech recognition, and echo detection for the estimation and compensation of convolutional distortions. To suggest what prompted the invention of the term cepstrum, this article narrates the historical and mathematical background that led to its discovery. The computations of earlier simple echo representations have shown that the spectrum representation domain results does not belong in the frequency or time domain. Bogert et al. (1963) chose to refer to it as quefrency domain and later termed the spectrum of the log of a time waveform as the cepstrum. The article also recounts the analysis of Al Oppenheim in relation to the cepstrum. It was in his theory for nonlinear signal processing, referred to as homomorphic systems, that the realization of the characteristic system of homomorphic convolution was reminiscent of the cepstrum. To retain both the relationship to the work of Bogart et al. and the distinction, the term power cepstrum was eventually applied to the nonlinear mapping in homomorphic deconvolution . While most of the terms in the glossary have faded into the background, the term cepstrum has survived and has become part of the digital signal processing lexicon.

Speaker normalization using efficient frequency warping procedures

Abstract: In an effort to reduce the degradation in speech recognition performance caused by variation in vocal tract shape among speakers, a frequency warping approach to speaker normalization is investigated. A set of low complexity, maximum likelihood based frequency warping procedures have been applied to speaker normalization for a telephone based connected digit recognition task. This paper presents an efficient means for estimating a linear frequency warping factor and a simple mechanism for implementing frequency warping by modifying the filter-bank in mel-frequency cepstrum feature analysis. An experimental study comparing these techniques to other well-known techniques for reducing variability is described. The results showed that frequency warping was consistently able to reduce word error rate by 20% even for very short utterances.

A frequency warping approach to speaker normalization

Abstract: In an effort to reduce the degradation in speech recognition performance caused by variation in vocal tract shape among speakers, a frequency warping approach to speaker normalization is investigated. A set of low complexity, maximum likelihood based frequency warping procedures have been applied to speaker normalization for a telephone based connected digit recognition task. This paper presents an efficient means for estimating a linear frequency warping factor and a simple mechanism for implementing frequency warping by modifying the filterbank in mel-frequency cepstrum feature analysis. An experimental study comparing these techniques to other well-known techniques for reducing variability is described. The results have shown that frequency warping is consistently able to reduce word error rate by 20% even for very short utterances.

Application of the wavelet transform for pitch detection of speech signals

Abstract: An event-detection pitch detector based on the dyadic wavelet transform is described. The proposed pitch detector is suitable for both low-pitched and high-pitched speakers and is robust to noise. Examples are provided that demonstrate the superior performance of the pitch detector in comparison with classical pitch detectors that use the autocorrelation and the cepstrum methods to estimate the pitch period. >