Proceedings of the 3rd International Universal Communication Symposium
A study on the generalization capability of acoustic models for robust speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Temporal modulation normalization for robust speech feature extraction and recognition
Multimedia Tools and Applications
Compensating the speech features via discrete cosine transform for robust speech recognition
ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
Probabilistic modulation spectrum factorization for robust speech recognition
ROCLING '11 ROCLING 2011 Poster Papers
Computers and Electrical Engineering
A multi-modal gesture recognition system using audio, video, and skeletal joint data
Proceedings of the 15th ACM on International conference on multimodal interaction
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In this paper, we study a novel technique that normalizes the modulation spectra of speech signals for robust speech recognition. The modulation spectra of a speech signal are the power spectral density (PSD) functions of the feature trajectories generated from the signal, hence they describe the temporal structure of the features. The modulation spectra are distorted when the speech signal is corrupted by noise. We propose the temporal structure normalization (TSN) filter to reduce the noise effects by normalizing the modulation spectra to reference spectra. The TSN filter is different from other feature normalization methods such as the histogram equalization (HEQ) that only normalize the probability distributions of the speech features. Our previous work showed promising results of TSN on a small vocabulary Aurora-2 task. In this paper, we conduct an inquiry into the theoretical and practical issues of the TSN filter that includes the following. 1) We investigate the effects of noises on the speech modulation spectra and show the general characteristics of noisy speech modulation spectra. The observations help to further explain and justify the TSN filter. 2) We evaluate the TSN filter on the Aurora-4 task and demonstrate its effectiveness for a large vocabulary task. 3) We propose a segment-based implementation of the TSN filter that reduces the processing delay significantly without affecting the performance. Overall, the TSN filter produces significant improvements over the baseline systems, and delivers competitive results when compared to other state-of-the-art temporal filters.