The most popular speech feature extractor used in automatic speech recognition (ASR) systems today is the mel frequency cepstral coefficient (mfcc) algorithm. Introduced in 1980, the filter bank-based algorithm eventually replaced linear prediction cepstral coefficients (lpcc) as the premier front end, primarily because of mfcc’s superior robustness to additive noise. However, mfcc does not app...

Two methods of spectral analysis for noisy speech recognition are proposed and tested in a speaker independent word recognition experiment under an additive white Gaussian noise environment. One is Mel-frequency cepstral coefficients (MFCC) spectral analysis on the autocorrelation sequence of the speech signal and the other is MFCC spectral analysis on its double autocorrelation sequence. The w...

This paper introduces a new articulation rate filter and reports its combination with recently proposed constant Q cepstral coefficients (CQCCs) in their first application to automatic speaker verification (ASV). CQCC features are extracted with the constant Q transform (CQT), a perceptually-inspired alternative to Fourier-based approaches to time-frequency analysis. The CQT offers greater freq...

In this paper we have studied two information fusion approaches, namely feature vector concatenation and decision fusion, for the task of reducing error rates in a speaker verification system used in mismatched conditions. Three types of features are fused: Mel Frequency Cepstral Coefficients (MFCC), MFCC with Cepstral Mean Subtraction (CMS) and Maximum Auto-Correlation Values (MACV). We have u...

The Mel-frequency cepstral coefficients (MFCC) are most widely used and successful features for speech recognition. But, their performance degrades in presence of additive noise. In this paper, we propose a noise compensation method for Mel filter bank energies and so MFCC features. This compensation method includes two steps: Mel sub-band spectral subtraction and then compression of Mel-Sub-ba...

In the study of speaker recognition, Mel Frequency Cepstral Coefficient (MFCC) method is the best and most popular which is used to feature extraction. Further vector quantization technique is used to minimize the amount of data to be handled in recent years. In the present study, the Speaker Recognition using Mel Frequency Cepstral coefficients and vector Quantization for the letter “Zha” (in ...

The objective of this work is to study the speaker-specific nature of analytic phase of speech signals. Since computation of analytic phase suffers from phase wrapping problem, we have used its derivativethe instantaneous frequency for feature extraction. The cepstral coefficients extracted from smoothed subband instantaneous frequencies (IFCC) are used as features for speaker verification. The...

Due to the increasing use of fusion in speaker recognition systems, one trend of current research activity focuses on new features that capture complementary information to the MFCC (Mel-frequency cepstral coefficients) for improving speaker recognition performance. The goal of this work is to combine (or fuse) amplitude and phase-based features to improve speaker verification performance. Base...

Identical acoustic features like Mel frequency cepstral Coefficients (MFCC)and Linear predictive cepstral coefficients (LPCC) are being widely used for different tasks like speech recognition and speaker recognition, whereas the requirement of speaker recognition is different than that of speech recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolutio...

The Mel-frequency cepstral coefficients (MFCC) are commonly used in speech recognition systems. But, they are high sensitive to presence of external noise. In this paper, we propose a noise compensation method for Mel filter bank energies and so MFCC features. This compensation method is performed in two stages: Mel sub-band filtering and then compression of Mel-sub-band energies. In the compre...