In the context of automatic speech recognition, the popular Mel Frequency Cepstral Coefficients(MFCC) as features, though perform very well under clean and matched environments, are observed to fail in mismatched conditions.The spectral maxima are often observed to preserve their locations and energies under noisy environments, but are not presented explicitly by the MFCC features. This paper p...

This paper presents the Automatic Genre Classification of Indian Tamil Music andWestern Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted fro...

We propose a method for integrating speech recognition and generation within a unified framework. The method consists of STRAIGHT, warped-frequency DCT, and an HMM engine. The warped-frequency DCT is used to derive a kind of mel-cepstral coefficient from the smoothed spectrum of STRAIGHT, which is known as a high-quality vocoder. This analysis/synthesis method has potential to improve the perfo...

Previous works have shown that remarkable performance improvements can be attained in speaker and language recognition tasks by combining several heterogeneous systems that provide complementary information. In this work, the complementarity of several i-vector language recognition systems, using Mel-Frequency Cepstral-Coefficient (MFCC) features computed on ShortTime Fourier Analysis windows o...

In this work, we investigate the use of discriminative models for automatic speech recognition of subvocalic speech via surface electromyography (sEMG). We also investigate the suitability of multiresolution analysis in the form of discrete wavelet transform (DWT) for sEMG-based speech recognition. We examine appropriate dimensionality reduction techniques for features extracted using different...

In this paper, the use of new auditory-based features derived from cochlear filters, have been proposed for classification of unvoiced fricatives. Classification attempts have been made to classify sibilant (i.e., /s/, /sh/) vs. non-sibilants (i.e., /f/, /th/) as well as for fricatives within each sub-category (i.e., intra-sibilants and intra-non-sibilants). Our experimental results indicate th...

The classification performance of an automatic classifier of voice pathology for the detection of normal and pathologic voice types is presented. The proposed classification system is non-intrusive and fully automated. Speech files of sustained phonation of the vowel sound /a/ in the 'Disordered Voice Database Model 4337' provided 631 subjects of both genders (58 normal, 573 pathologic). This d...

Mel-frequency cepstral coefficients (MFCC) have traditionally been used in speaker identification applications. Their use has been extended to speech quality assessment for clinical applications during the last few years. While the significance of such parameters for such an application may not seem clear at first thought, previous research has demonstrated their robustness and statistical sign...

Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract ...

Speech babble is one of the most challenging noise interference due to its speaker/speech like characteristics for speech and speaker recognition systems. Performance of such systems strongly degrades in the presence of background noise, like the babble noise. Existing techniques solve this problem by additional processing of speech signal to remove noise. In contrast to existing works, the aim...