OBJECTIVES The vocal changes after a thyroidectomy are temporary and nonsevere, therefore, obtaining accurate analytical results on the pathological vocal characteristics following such a procedure is difficult. For a more objective acoustic analysis, this study used the cepstral analysis method to examine changes in the patients' voices during the perioperative period regarding sustained vowel...

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral c...

Speech model combination with the background noise has been shown effective to improve the pattern classification rate of noisy speech. The model combination can be performed by the addition of the spectral statistics such as the means and the variances. Since the speech feature for pattern classification has to be expressed in the cepstral domain, the combined spectral statistics have to be tr...

Random fields play a central role in the analysis of spatially correlated data and, as a result, have a significant impact on a broad array of scientific applications. This paper studies the cepstral random field model, providing recursive formulas that connect the spatial cepstral coefficients to an equivalent moving-average random field, which facilitates easy computation of the autocovarianc...

Spectral domain speech enhancement algorithms based on nonnegative spectrogram models such as non-negative matrix factorization (NMF) and non-negative matrix factor deconvolution are powerful in terms of signal recovery accuracy, however they do not directly lead to an enhancement in the feature domain (e.g., cepstral domain) or in terms of perceived quality. We have previously proposed a metho...

Problem statement: The fundamental frequency (F0) of the human speech corresponds to the vibration frequency of the human vocal chords. To extract the F0 from a speech utterance, one approach is based on the Cepstral analysis. In Thai, there are four main dialects spoken by Thai people residing in four core region including central, north, northeast and south regions. Environmental noises are a...

Obtaining a compact, information-rich representation of the speech signal is an important first step in ASR. A large majority of ASR systems use some form of cepstral coefficients for this purpose. Computation of these cepstral coefficients typically includes several of the following steps: (1) Highfrequency preemphasis, using an FIR filter of the form y(k) = x(k) ax(k-1), with a taking values ...

In this paper we introduce a robust feature extractor, dubbed as robust compressive gammachirp filterbank cepstral coefficients (RCGCC), based on an asymmetric and level-dependent compressive gammachirp filterbank and a sigmoid shape weighting rule for the enhancement of speech spectra in the auditory domain. The goal of this work is to improve the robustness of speech recognition systems in ad...

In this article, new feature extraction methods, which utilize wavelet decomposition and reduced order linear predictive coding (LPC) coefficients, have been proposed for speech recognition. The coefficients have been derived from the speech frames decomposed using discrete wavelet transform. LPC coefficients derived from subband decomposition (abbreviated as WLPC) of speech frame provide bette...

This paper describes a speaker identification system that uses complementary acoustic features derived from the vocal source excitation and the vocal tract system. Conventional speaker recognition systems typically adopt the cepstral coefficients, e.g., Mel-frequency cepstral coefficients (MFCC) and linear predictive cepstral coefficients (LPCC), as the representative features. The cepstral fea...