Erik Visser

A spatio-temporal speech enhancement scheme for robust speech recognition in noisy environments

Abstract A new speech enhancement scheme is presented integrating spatial and temporal signal processing methods for robust speech recognition in noisy environments. The scheme first separates spatially localized point sources from noisy speech signals recorded by two microphones. Blind source separation algorithms assuming no a priori knowledge about the sources involved are applied in this spatial processing stage. Then denoising of distributed background noise is achieved in a combined spatial/temporal processing approach. The desired speaker signal is first processed along with an artificially constructed noise signal in a supplementary blind source separation step. It is further denoised by exploiting differences in temporal speech and noise statistics in a wavelet filterbank. The scheme’s performance is illustrated by speech recognition experiments on real recordings in a noisy car environment. In comparison to a common multi-microphone technique like beamforming with spectral subtraction, the scheme is shown to enable more accurate speech recognition in the presence of a highly interfering point source and strong background noise.

Blind source separation in mobile environments using a priori knowledge

A speech enhancement scheme including blind source separation and background denoising based on minimum statistics is studied in mobile environments. To accommodate the dependence of the separated output signals on the spatial properties of the recorded source signals, these blind signal processing steps are complemented by an adaptive separated output channel selection stage using prior knowledge about the desired speaker speech content. The resulting scheme performance is illustrated by speech recognition experiments on real recordings corrupted by various noise sources and shown to outperform conventional beamforming and single channel denoising techniques as well as an equivalent scheme with fixed output channel selection.

Frequency Domain Passive Broadband Speaker Localization using a Permutation-Free Blind Source Separation Algorithm

Traditional passive broadband source localization techniques like maximum likelihood estimation and MUSIC have shown difficulties in situations where multiple correlating source signals are interfering with each other. Blind source separation (BSS) algorithms on the other hand have demonstrated good performance in separating correlated mixture signals into independent sources. In this paper it will be shown that the performance of traditional source localization algorithms can be improved by using a permutation-free frequency domain BSS algorithm as a front end. In addition a source localization method based solely on information gained from the separated BSS solution and sensor array architecture is presented. The methodologies are illustrated in an undercomplete acoustic scenario involving 3 speech sources and a 6 element microphone array.