This paper provides an efficient approach for text-independent speaker identification using the Inverted Mel-frequency Cepstral Coefficients as feature set and Finite Doubly Truncated Gaussian Mixture as Model (FDTGMM). Over the years, Mel-Frequency Cepstral Coefficients (MFCC), modeled on the human auditory system, has been used as a standard acoustic feature set for speech related application...

Lung sounds convey useful information related to pulmonary pathology. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Motivated by the success of cepstral features in speech signal classification, we evaluate five different cepstral features to recognize three types of lung sounds: norma...

Cepstral-based acoustic cues for disordered voices analysis have been investigated in a number of studies. It has been shown that cepstral-based acoustic cues such as the harmonics-to-noise ratio (HNR), the amplitude of the first rhamonic (R1A) provide acoustic correlates for hoarse voice quality. The aim of this presentation is to investigate an acoustic analysis of speech by means of spectral...

In this paper, we propose several compensation approaches to alleviate the effect of additive noise on speech features for speech recognition. These approaches are simple yet efficient noise reduction techniques that use online constructed pseudo stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transforms for noise-corrupted speech features to...

Cepstral normalization has been popularly used as a powerful approach to produce robust features for speech recognition. Good examples of approaches in this family include the well known Cepstral Mean Subtraction (CMS) and Cepstral Mean and Variance Normalization (CMVN), in which either the first or both the first and the second moments of the Mel-frequency Cepstral Coefficients (MFCCs) are nor...

Most speech models represent the static and derivative cepstral features with separate models that can be inconsistent with each other. In our previous work, we proposed the hidden spectral peak trajectory model (HSPTM) in which the static cepstral trajectories are derived from a set of hidden trajectories of the spectral peaks (captured as spectral poles) in the time-frequency domain. In this ...

Previous work has shown that good accuracy improvements can be made for isolated word recognition using cepstral-time matrices as the speech feature instead of the more conventional MFCC-based speech feature augmented with higher order cep-strum. This work extends the performance improvements to UK English connected digit strings and to a sub-word based town names task. Experimental results are...

In this paper a method of text-independent speaker recognition using discrete vector quantization is presented. The identification experiments were performed in a closed set of 599 speakers and two various types of features were tested: cepstral mean subtraction coefficients and mel-frequency cepstral coefficients. The effect of the various codebook size on the speaker identification performanc...

We proposed an augmented cepstral mean normalization algorithm that differentiates noise and speech during normalization, and computes a different mean for each. The new procedure reduced the error rate slightly for the case of sameenvironment testing, and significantly reduced the error rate by 25% when an environmental mismatch exists over the case of standard cepstral mean normalization.

In this paper, we present robust feature extractors that incorporate a regularized minimum variance distortionless response (RMVDR) spectrum estimator instead of the discrete Fourier transform-based direct spectrum estimator, used in many front-ends including the conventional MFCC, to estimate the speech power spectrum. Direct spectrum estimators, e.g., single tapered periodogram, have high var...