Microphone Array Based Speech Recognition with Different Talker-Array Positions
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Microphone array processing for robust speech recognition
Microphone array processing for robust speech recognition
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Speech Enhancement (Signal Processing and Communications)
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Speech Recognition over Digital Channels: Robustness And Standards
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A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
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Springer Handbook of Speech Processing
Text-independent speaker recognition using graph matching
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Histogram Equalization Utilizing Window-Based Smoothed CDF Estimation for Feature Compensation
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Higher order cepstral moment normalization for improved robust speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Multichannel Cepstral Domain Feature Warping for Robust Speech Recognition
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Comparative evaluation of single-channel MMSE-Based noise reduction schemes for speech recognition
Journal of Electrical and Computer Engineering
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IEEE Transactions on Audio, Speech, and Language Processing
Robust Speech Recognition Using a Cepstral Minimum-Mean-Square-Error-Motivated Noise Suppressor
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Robust Speaker Recognition in Noisy Conditions
IEEE Transactions on Audio, Speech, and Language Processing
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Feature statistics normalization in the cepstral domain is one of the most performing approaches for robust automaticspeech and speaker recognition in noisy acoustic scenarios: feature coefficients are normalized by using suitable linear or nonlinear transformations in order to match the noisy speech statistics to the clean speech one. Histogram equalization (HEQ) belongs to such a category of algorithms and has proved to be effective on purpose and therefore taken here as reference. In this paper the presence of multi-channel acoustic channels is used to enhance the statistics modeling capabilities of the HEQ algorithm, by exploiting the availability of multiple noisy speech occurrences, with the aim of maximizing the effectiveness of the cepstra normalization process. Computer simulations based on the Aurora 2 database in speech and speaker recognition scenarios have shown that a significant recognition improvement with respect to the single-channel counterpart and other multi-channel techniques can be achieved confirming the effectiveness of the idea. The proposed algorithmic configuration has also been combined with the kernel estimation technique in order to further improve the speech recognition performances.