An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

Time-Frequency Analysis.

Extraction of speaker-specific excitation information from linear prediction residual of speech

Vowel onset point (VOP) is the instant at which the onset of vowel takes place during speech production. There are significant changes occurring in the energies of excitation source, spectral peaks, and modulation spectrum at the VOP. This paper demonstrates the independent use of each of these three energies in detecting the VOPs. Since each of these energies represents a different aspect of speech production, it may be possible that they contain complementary information about the VOP. The individual evidences are therefore combined for detecting the VOPs. The error rates measured as the ratio of missing and spurious to the total number of VOPs evaluated on the sentences taken from the TIMIT database are 6.92%, 8.8%, 6.13%, and 4.0% for source, spectral peaks, modulation spectrum, and combined information, respectively. The performance of the combined method for VOP detection is improved by 2.13% compared to the best performing individual VOP detection method.

Vowel Onset Point Detection Using Source, Spectral Peaks, and Modulation Spectrum Energies

This paper provides a review of multimodal biometric person authentication systems. The paper begins with an introduction to biometrics, its advantages, disadvantages, and authentication system using them. A brief discussion on the selection criteria of different biometrics is also given. This is followed by a discussion on the classification of biometric systems, their strengths, and limitations. Detailed descriptions on the multimodal biometric person authentication system, different modes of operation, and integration scenarios are also provided. Considering the importance of information fusion in multi-biometric approach, a separate section is dedicated on the different levels of fusion, which include sensor-level, feature-level, score-level, rank-level, and abstract-level fusions, and also different rules of fusion. This paper also presents an overview of some performance parameters and error rates for biometric person authentication systems. A separate section is devoted to the recent trends...

Multimodal Biometric Person Authentication : A Review

This paper proposes an approach for processing speech from multiple microphones to enhance speech degraded by noise and reverberation. The approach is based on exploiting the features of the excitation source in speech production. In particular, the characteristics of voiced speech can be used to derive a coherently added signal from the linear prediction (LP) residuals of the degraded speech data from different microphones. A weight function is derived from the coherently added signal. For coherent addition the time-delay between a pair of microphones is estimated using the knowledge of the source information present in the LP residual. The enhanced speech is generated by exciting the time varying all-pole filter with the weighted LP residual.

Speech enhancement using excitation source information

This work describes the techniques used for spoofed speech detection for the ASVspoof 2017 challenge. The main focus of this work is on exploiting the differences in the speech-specific nature of genuine speech signals and spoofed speech signals generated by replay attacks. This is achieved using glottal closure instants, epoch strength, and the peak to side lobe ratio of the Hilbert envelope of linear prediction residual. Apart from these source features, the instantaneous frequency cosine coefficient feature, and two cepstral features namely, constant Q cepstral coefficients and mel frequency cepstral coefficients are used. A combination of all these features is performed to obtain a high degree of accuracy for spoof detection. Initially, efficacy of these features are tested on the development set of the ASVspoof 2017 database with Gaussian mixture model based systems. The systems are then fused at score level which acts as the final combined system for the challenge. The combined system is able to outperform the individual systems by a significant margin. Finally, the experiments are repeated on the evaluation set of the database and the combined system results in an equal error rate of 13.95%.

S. R. Mahadeva Prasanna

Papers

Extraction of speaker-specific excitation information from linear prediction residual of speech

Vowel Onset Point Detection Using Source, Spectral Peaks, and Modulation Spectrum Energies

Multimodal Biometric Person Authentication : A Review

Speech enhancement using excitation source information

Spoof detection using source, instantaneous frequency and cepstral features