Fully adaptive SVD-based noise removal for robust speech recognition

This paper deals with the problem of the recognition of speech corrupted by additive noise at moderate SNR ratios. The proposed technique - based on Singular Value Decomposition (SVD), and fully adaptive - outperforms well-known approaches as Nonlinear Spectral Estimation and SNR-Normalisation for the recognition of large vocabulary continuous speech. Current techniques for robust speech recognition take advantage of slowly varying and/or accurately modeled environments. Deviations from these prior assumptions greatly compromise the performance. Our new approach is based on SVD and tries to overcome these limitations. This technique automatically removes additive noise by suppressing low energetic, noise related, singular components of the Hankel matrix constructed from the original signal. Providing the SVD-algorithm with prior knowledge about the noise highly improves the efficiency. The algorithm is fully adaptive, and works in real-time. Recognition experiments on a database with large vocabulary, continuous speech (Resource Management) show that the WER is more than halved.Hermus K., Wambacq P., Van Compernolle D., "Fully adaptive SVD-based noise removal for robust speech recognition", Proceedings Workshop on robust methods for speech recognition in adverse conditions, pp. 223-226, May 25-26, 1999, Tampere, Finland.status: publishe

Fully adaptive SVD-based noise removal for robust speech recognition

This paper presents a new approach to improve the robustness of large vocabulary continuous speech recognition. The proposed technique - based on Singular Value Decomposition (SVD) - originates from classical signal enhancement, but it is adapted to the specific requirements imposed by the speech recognition process. Additive noise reduction is obtained by altering the singular value spectrum of the signal observation matrix, thereby preserving speech signal components and suppressing noise-related components. The basic algorithms are developed for white noise but they can easily be extended to the general coloured noise case. With the aid of a noise reference, non-stationary noise can be handled as well. All schemes are adaptive, and work in real-time. Recognition experiments on a noise-corrupted database with large vocabulary, continuous speech (Resource Management) reveal that relative reductions of the WER of more than 60% are obtained.Proceedings Eurospeech'99, 6th European Conference on Speech Communication and Technology, pp. 1951-1954, September 5-9, 1999, Budapest, Hungarystatus: publishe