Fundamentals of speech recognition
Fundamentals of speech recognition
The Modulation Spectrogram: In Pursuit of an Invariant Representation of Speech
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Joint acoustic and modulation frequency
EURASIP Journal on Applied Signal Processing
Integration of audiovisual sensors and technologies in a smart room
Personal and Ubiquitous Computing
Probabilistic amplitude demodulation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
New insights into the noise reduction Wiener filter
IEEE Transactions on Audio, Speech, and Language Processing
MVA Processing of Speech Features
IEEE Transactions on Audio, Speech, and Language Processing
Optimization of temporal filters for constructing robust features in speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Normalization of the Speech Modulation Spectra for Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Modulation domain blind speech separation in noisy environments
Speech Communication
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Traditionally, noise reduction methods for additive noise have been quite different from those for reverberation. In this study, we investigated the effect of additive noise and reverberation on speech on the basis of the concept of temporal modulation transfer. We first analyzed the noise effect on the temporal modulation of speech. Then on the basis of this analysis, we proposed a two-stage processing algorithm that adaptively normalizes the temporal modulation of speech to extract robust speech features for automatic speech recognition. In the first stage of the proposed algorithm, the temporal modulation contrast of the cepstral time series for both clean and noisy speech is normalized. In the second stage, the contrast normalized temporal modulation spectrum is smoothed in order to reduce the artifacts due to noise while preserving the information in the speech modulation events (edges). We tested our algorithm in speech recognition experiments for additive noise condition, reverberant condition, and noisy condition (both additive noise and reverberation) using the AURORA-2J data corpus. Our results showed that as part of a uniform processing framework, the algorithm helped achieve the following: (1) for the additive noise condition, a 55.85% relative word error reduction (RWER) rate when clean conditional training was performed, and a 41.64% RWER rate when multi-conditional training was performed, (2) for the reverberant condition, a 51.28% RWER rate, and (3) for the noisy condition (both additive noise and reverberation), a 95.03% RWER rate. In addition, we evaluated the performance of each stage of the proposed algorithm in AURORA-2J and AURORA4 experiments, and compared the performance of our algorithm with the performances of two similar processing algorithms in the second stage. The evaluation results further confirmed the effectiveness of our proposed algorithm.