[[alternative]]Text-Independent Speaker Identification Systems Based on Multi-Layer Gaussian Mixture Models

計畫編號：NSC92-2213-E032-026研究期間：200308~200407研究經費：541,000[[sponsorship]]行政院國家科學委員

A Subband-Based SVM Front-End for Robust ASR

This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels

Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions

Wavelet speech enhancement based on time-scale adaptation

Abstract : We propose a new speech enhancement method based on time and scale adaptation of wavelet thresholds. The time dependency is introduced by approximating the Teager Energy of the wavelet coefficients, while the scale dependency is introduced by extending the principle of level dependent threshold to Wavelet Packet Thresholding. This technique does not require an explicit estimation of the noise level or of the apriori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MELscale decomposition based on wavelet packets is also compared to the common wavelet packet scale. Comparison in terms of Signal-to-Noise Ratio (SNR) is reported for time adaptation and time-scale adaptation thresholding of the wavelet coefficients thresholding. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models Speech recognition experiments are conducted on the AURORA–2 database and show that the proposed method improves the speech recognition rates for low SNRs