Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2007 Article ID 65420 13 pages doi 2007 65420 Research Article An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition Bhiksha Raj 1 Lorenzo Turicchia 2 Bent Schmidt-Nielsen 1 and Rahul Sarpeshkar2 1 Mitsubishi Electric Research Laboratories MERL 201 Broadway Cambridge MA 02139-4307 USA 2 Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA Received 29 November 2006 Revised 14 March 2007 Accepted 23 April 2007 Recommended by Stephen Voran We describe an FFT-based companding algorithm for preprocessing speech before recognition. The algorithm mimics tone-to-tone suppression and masking in the auditory system to improve automatic speech recognition performance in noise. Moreover it is also very computationally efficient and suited to digital implementations due to its use of the FFT. In an automotive digits recognition task with the CU-Move database recorded in real environmental noise the algorithm improves the relative word error by at -5 dB signal-to-noise ratio SNR and by across allSNRs -5 dB SNR to 15 dB SNR . In the Aurora-2 database recorded with artificially added noise in several environments the algorithm improves the relative word error rate in almost all situations. Copyright 2007 Bhiksha Raj et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION The performance of humans on speech recognition tasks in noise is extraordinary compared to state-of-the-art automatic speech recognition ASR systems 1 . One explanation is that the brain has amazing pattern recognition abilities not well captured by ASR systems. Additionally the auditory periphery has sophisticated signal representations which are highly robust to noise. .
