Low Complexity Spectral Imputation for Noise Robust Speech Recognition

of the Thesis Low Complexity Spectral Imputation for Noise Robust Speech Recognition by Julien van Hout Master of Science in Electrical Engineering University of California, Los Angeles, 2012 Professor Abeer Alwan, Chair With the recent push of Automatic Speech Recognition (ASR) capabilities to mobile devices, the user’s voice is now recorded in environments with a potentially high level of background noise. To reduce the sensitivity of ASR performance to these distortions, techniques have been proposed that preprocess the speech waveforms to remove noise effects while preserving discriminative speech information. At the expense of increased complexity, recent algorithms have significantly improved recognition accuracy but remain far from human performance in highly noisy environments. With a concern for both complexity and performance, this thesis investigated ways to reduce the corruptive effect of noise by directly weighting the powerspectrum (SMF pow ) or log-spectrum (SMF log ) of speech by a mask whose values are within [0,1] and are indexed on the local relative prominence of speech and noise energy. Additional contributions include a low-complexity approach to mask estimation and the use of spectral flooring for matching the dynamic range of clean and noisy spectra. These two techniques are evaluated on two standard noisy ASR databases: the Aurora-2 connected digits recognition task with 11 ii words, and the Aurora-4 continuous speech recognition task with 5000 words. On the Aurora-2 task, the SMF log algorithm leads to state-of-the-art performance, with a limited complexity compared to existing techniques. The SMF