T. K. Basu

A New Transform for Robust Text-Independent Speaker Identification

This paper proposes a new method of feature extraction for robust text-independent speaker identification. The focus of this work is on applications which yield higher identification accuracy without increasing the computational effort. The impetus for this new feature extraction technique comes from a new transformation. We have proposed this transform from speaker identification perspective. A complete experimental evaluation was conducted on a database of 61 speakers with Gaussian mixture speaker model. This new feature extraction technique has been compared with mel-frequency cepstral coefficient (MFCC) feature. Evaluation results show, that the new feature provides better identification accuracy than the MFCC feature. The discrimination capability of the feature sets have been evaluated statistically, using F-ratio and J- measure. Experimental results show that the new feature set is much more discriminative than the MFCC feature set.

A New Nyquist Window with Near Optimal Time-Bandwidth Product

This paper introduces a new Nyquist window. The proposed window has been compared with the Gaussian window. The time-bandwidth product of the proposed window is very close to the time-bandwidth product of the Gaussian window.

Design of P-I-D power system stabilizer for multimachine system

In the present work, dynamic stability analysis of power system is investigated considering proportional-integral-derivative power system stabilizer (P-I-D PSS) for multimachine power systems. Gain settings of P-I-D PSS are optimized by minimizing an objective function using genetic algorithm (GA). Dynamic responses are also compared considering P-I-D PSS and conventional phase lead-lag power system stabilizer (CPSS). Attempt is also made to examine whether gain settings of P-I-D PSS obtained at a particular operating condition works well or not to all other operating conditions. Analysis reveals that the P-I-D PSS gives better dynamic performances as compared to that of conventional power system stabilizer. Analysis also reveals that the optimum gain settings of P-I-D PSS obtained at nominal operating condition works well to other operating conditions without much deteriorating the dynamic responses.

Text-Independent Speaker Identification in Emotional Environments: A Classifier Fusion Approach

The study of text-independent speaker identification in emotional environments is presented in this paper. The study includes identifying the speaker using speech samples in five basic emotions viz. anger, happiness, sadness, disgust, and fear. The work presented compares the performance of four feature sets: Mel frequency cepstral coefficients (MFCC), Line spectral frequencies (LSF), Teager energy based mel cepstral coefficients (TMFCC) and Temporal energy of subband cepstral coefficients (TESBCC). Next, the performance of the speaker identification is studied with combination of two features MFCC-LSF and TESBCC-LSF. A novel classifier fusion method is proposed and its performance is compared with that of the individual classifiers. The database containing speech utterances recorded in the five basic emotions from thirty four speakers in one of the Indian languages (Marathi) is used for experimentation. Gaussian mixture model is used for classification. Fusion of classifiers enhances the speaker identification accuracy in both emotional and neutral environments.

Significant improvement in the closed set text-independent speaker identification using features extracted from Nyquist filter bank

This paper introduces the use of a new method of feature extraction for robust text-independent speaker identification. The focus of this work is on applications which yield higher identification accuracy without increasing the computational effort. The impetus for this new feature extraction technique comes from a new transformation which is based on the Nyquist filter bank. We have proposed this transform from speaker identification perspective. This new feature extraction technique has been compared with Mel-frequency cepstral coefficient (MFCC) feature both theoretically and practically. Experimental evaluation was conducted on POLYCOST database with 130 speakers using Gaussian mixture speaker model. On clean speech the proposed feature set has 11.5% higher average accuracy compared to the MFCC feature set. For noisy speech also the proposed feature set performs significantly better than the MFCC feature set.

Signal phase recovery using the bispectrum

Abstract In this paper, three simple algorithms for recovery of the phase of a discrete deterministic signal from its bispectrum have been proposed. The algorithms do not involve any phase unwrapping or any solution of a system of equations. Even though the discussion presented here is for deterministic signals, it has been shown that the algorithms can be used for estimation of the phase of linear stochastic processes as well.

Phase-only blind deconvolution using bicepstrum iterative reconstruction algorithm (BIRA)

In this correspondence, we propose a procedure based on the bicepstrum iterative reconstruction algorithm (BIRA) for the reconstruction of an input signal (unobservable) of a linear-phase or zero-phase linear time-invariant (LTI) system from its observed output. The desired input signal is reconstructed without the knowledge of the system transfer function.

Temporal energy and correlation features from Nyquist filter bank for text-independent speaker identification

This paper demonstrates the use of two new methods of feature extraction called temporal energy of subband cepstral coefficient (TESBCC) and temporal correlation of subband cepstral coefficient (TCSBCC) for text-independent speaker identification. The focus of this work is on applications which yield higher identification accuracy without increasing the computational effort. The impetus for these new feature extraction techniques comes from a new transformation which is based on the Nyquist filter bank. We have proposed this transformation from speaker identification perspective. TESBCC and TCSBCC have been compared with Mel-frequency cepstral coefficient (MFCC) feature both theoretically and practically. Experimental evaluation was conducted on POLYCOST database with 130 speakers using Gaussian mixture speaker model. TESBCC feature set has 7.88% higher average accuracy compared to the MFCC feature set. Similarly TCSBCC feature set has 5.23% higher average accuracy compared to the MFCC feature set.

Features extracted using frequency-time analysis approach from Nyquist filter bank and Gaussian filter bank for text-independent speaker identification

This paper compares the feature sets extracted using frequency-time analysis approach and time-frequency analysis approach for text-independent speaker identification. The impetus for the frequency-time analysis approach comes from the band pass filtering view of STFT. Nyquist filter bank and Gaussian filter bank both have been used for extracting features using frequency-time analysis approach. Experimental evaluation was conducted on the POLYCOST database with 130 speakers using Gaussian mixture speaker model. Results reveal that, the feature sets extracted using frequency-time analysis approach performs significantly better compared to the feature set extracted using time-frequency analysis approach.

Analysis of dynamic stability of power system using thyristor controlled phase shifter

In this paper, the application of thyristor controlled phase shifter (TCPS) in damping power system oscillation is investigated. Analysis is carried out considering TCPS equipped with conventional lead-lag controller and with proportional-integral-derivative (P-I-D) controller. Parameters and gain settings of the TCPS controller are optimized using genetic algorithm (GA). Dynamic performances considering conventional phase lead-lag power system stabilizer (CPSS) and TCPS are compared. Analysis reveals that TCPS equipped with P-I-D controller improves the dynamic performances significantly as compared to that of conventional power system stabilizer.