Classification of speech under stress using target driven features
Speech Communication - Special issue on speech under stress
Speech Communication - Special issue on speech under stress
Speaker Normalization Based on Frequency Warping
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Analysis and compensation of stressed and noisy speech with application to robust automatic recognition
A parametric approach to vocal tract length normalization
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Speaker normalization using efficient frequency warping procedures
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Unsupervised equalization of Lombard effect for speech recognition in noisy adverse environment
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
Quantile based histogram equalization for noise robust large vocabulary speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
International Journal of Speech Technology
Maximum Likelihood Acoustic Factor Analysis Models for Robust Speaker Verification in Noise
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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In the presence of environmental noise, speakers tend to adjust their speech production in an effort to preserve intelligible communication. The noise-induced speech adjustments, called Lombard effect (LE), are known to severely impact the accuracy of automatic speech recognition (ASR) systems. The reduced performance results from the mismatch between the ASR acoustic models trained typically on noise-clean neutral (modal) speech and the actual parameters of noisy LE speech. In this study, novel unsupervised frequency domain and cepstral domain equalizations that increase ASR resistance to LE are proposed and incorporated in a recognition scheme employing a codebook of noisy acoustic models. In the frequency domain, short-time speech spectra are transformed towards neutral ASR acoustic models in a maximum-likelihood fashion. Simultaneously, dynamics of cepstral samples are determined from the quantile estimates and normalized to a constant range. A codebook decoding strategy is applied to determine the noisy models best matching the actual mixture of speech and noisy background. The proposed algorithms are evaluated side by side with conventional compensation schemes on connected Czech digits presented in various levels of background car noise. The resulting system provides an absolute word error rate (WER) reduction on lO-dB signal-to-noise ratio data of 8.7% and 37.7 % for female neutral and LE speech, respectively, and of 8.7% and 32.8% for male neutral and LE speech, respectively, when compared to the baseline recognizer employing perceptual linear prediction (PLP) coefficients and cepstral mean and variance normalization.