Multivariate Cepstral Feature Compensation on Band-limited Data for Robust Speech Recognition

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 144-151

Exploring sub-band cepstral distances for more robust speaker classification

This paper presents the first of two-part exploration into the potential of parametric cepstral distance (PCD) as a forensic voice comparison feature, based on Japanese vowel data collected from 306 male native speakers under microphone and mobile transmission conditions. The behaviours of PCDs were closely examined by altering sub-band settings, and we found the behaviour of PCDs to correspond well to what is known about formants, which suggests that PCDs are relatable to articulatory gestures. Comparison between sub-band and full-band PCD revealed that limiting the band range to a specific frequency region makes the feature more robust against channel mismatch, encouraging further examination of this potential feature.The work presented here was partly supported by JSPS KAKENHI Grant Number JP18H01671, JP25350488

Robust speech recognition under band-limited channels and other channel distortions

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 200

Wavelet-based techniques for speech recognition

In this thesis, new wavelet-based techniques have been developed for the extraction of features from speech signals for the purpose of automatic speech recognition (ASR). One of the advantages of the wavelet transform over the short time Fourier transform (STFT) is its capability to process non-stationary signals. Since speech signals are not strictly stationary the wavelet transform is a better choice for time-frequency transformation of these signals. In addition it has compactly supported basis functions, thereby reducing the amount of computation as opposed to STFT where an overlapping window is needed. [Continues.

Forensic voice comparison using sub-band cepstral distances as features: A first attempt with vowels from 306 Japanese speakers under channel mismatch conditions

This study presents the latter part of an exploratory study of the potential of sub-band parametric cepstral distance (PCD) as an alternative forensic voice comparison (FVC) feature to formants and cepstral coefficients. Using 5 Japanese vowels produced by 306 male Japanese speakers, we conducted LRbased FVC experiments under a channel mismatch condition, with sub-bands selected in reference to the expected formant locations. Combining 3 sub-band PCDs from F1, F2, and F3 ranges, sub-band PCDs outperformed the full-band PCDs in speaker classification, demonstrating their promise as an automatically extractable, robust, and linguistically interpretable acoustic feature for FVC. Index Terms: Sub-band cepstral distance, likelihood ratio, forensic voice comparison, channel mismatch, Japanese vowelsThe work presented here was partly supported by JSPS KAKENHI Grant Number JP18H01671, JP25350488

Studies on noise robust automatic speech recognition

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK

Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

ASR Systems in Noisy Environment: Analysis and Solutions for Increasing Noise Robustness

This paper deals with the analysis of Automatic Speech Recognition (ASR) suitable for usage within noisy environment and suggests optimum configuration under various noisy conditions. The behavior of standard parameterization techniques was analyzed from the viewpoint of robustness against background noise. It was done for Melfrequency cepstral coefficients (MFCC), Perceptual linear predictive (PLP) coefficients, and their modified forms combining main blocks of PLP and MFCC. The second part is devoted to the analysis and contribution of modified techniques containing frequency-domain noise suppression and voice activity detection. The above-mentioned techniques were tested with signals in real noisy environment within Czech digit recognition task and AURORA databases. Finally, the contribution of special VAD selective training and MLLR adaptation of acoustic models were studied for various signal features