452 research outputs found
A Novel Robust Mel-Energy Based Voice Activity Detector for Nonstationary Noise and Its Application for Speech Waveform Compression
The voice activity detection (VAD) is crucial in all kinds of speech applications. However, almost all existing VAD algorithms suffer from the nonstationarity of both speech and noise. To combat this difficulty, we propose a new voice activity detector, which is based on the Mel-energy features and an adaptive threshold related to the signal-to-noise ratio (SNR) estimates. In this thesis, we first justify the robustness of the Bayes classifier using the Mel-energy features over that using the Fourier spectral features in various noise environments. Then, we design an algorithm using the dynamic Mel-energy estimator and the adaptive threshold which depends on the SNR estimates. In addition, a realignment scheme is incorporated to correct the sparse-and-spurious noise estimates. Numerous simulations are carried out to evaluate the performance of our proposed VAD method and the comparisons are made with a couple existing representative schemes, namely the VAD using the likelihood ratio test with Fourier spectral energy features and that based on the enhanced time-frequency parameters. Three types of noise, namely white noise (stationary), babble noise (nonstationary) and vehicular noise (nonstationary) were artificially added by the computer for our experiments. As a result, our proposed VAD algorithm significantly outperforms other existing methods as illustrated by the corresponding receiver operating curves (ROCs). Finally, we demonstrate one of the major applications, namely speech waveform compression, associated with our new robust VAD scheme and quantify the effectiveness in terms of compression efficiency
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
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
Improving the robustness of the usual fbe-based asr front-end
All speech recognition systems require some form of signal representation that parametrically models the
temporal evolution of the spectral envelope. Current parameterizations involve, either explicitly or implicitly, a
set of energies from frequency bands which are often distributed in a mel scale. The computation of those filterbank
energies (FBE) always includes smoothing of basic spectral measurements and non-linear amplitude
compression. A variety of linear transformations are typically applied to this time-frequency representation prior
to the Hidden Markov Model (HMM) pattern-matching stage of recognition. In the paper, we will discuss some
robustness issues involved in both the computation of the FBEs and the posterior linear transformations,
presenting alternative techniques that can improve robustness in additive noise conditions. In particular, the root
non-linearity, a voicing-dependent FBE computation technique and a time&frequency filtering (tiffing)
technique will be considered. Recognition results for the Aurora database will be shown to illustrate the potential
application of these alternatives techniques for enhancing the robustness of speech recognition systems.Peer ReviewedPostprint (published version
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
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