Real-Time Implementation of Blind Spatial Subtraction Array For Hands-Free Robot Spoken Dialogue System

IROS2008: IEEE/RSJ International Conference on Intelligent Robotics and Systems , September 22-26, 2008, Nice, France.In this paper, we construct a hands-free robot spoken dialogue system based on the real-time blind spatial subtraction array (BSSA) and evaluate the system. BSSA is the blind source extraction method, and the source extraction in BSSA is carried out by subtracting the power spectrum of the estimated noise signal by the independent component analysis from the power spectrum of the target speech partly enhanced signal. Although BSSA can reduce noise signal efficiently, ICA consumes huge amount of computational costs. Thus it is difficult to run BSSA in real-time. In this paper, we newly propose a real-time architecture of BSSA and construct a hands-free robot spoken dialogue system based on the real-time BSSA. In the hands-free robot spoken dialogue system with the real-time BSSA, 6% improvement of the speech recognition result can be seen compared with the conventional speech enhancement methods

MMSE STSA estimator with nonstationary noise estimation based on ICA for high-quality speech enhancement

ICASPP2010: IEEE International Conference on Acoustics, Speech, and Signal Processing, March 14-19, 2010, Dallas, Texas, USA.In this paper, we propose a new blind speech extraction method consisting of a minimum mean-square error short-time spectral amplitude (MMSE STSA) estimator and noise estimation based on independent component analysis (ICA). First, we perform a computer simulation using the artificial noise whose stationarity could be controlled parametrically, and the obtained results indicate that the proposed method is superior to conventional methods, such as blind spatial subtraction array (BSSA) and the original MMSE STSA estimator under the non-point-source and nonstationary noise condition. Finally, we conduct an experiment in an actual railway-station environment, and objective and subjective evaluations to confirm the advantage of the proposed method in the real world

Permutation-Robust Structure for ICA-Based Blind Source Extraction

ICASSP2007: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 15-20, 2007, Honolulu, Hawaii, USA.In this paper, we investigate a new blind source separation (BSS) structure from a permutation-robustness viewpoint, to mitigate the permutation problem which commonly arises in frequency-domain independent component analysis (ICA). Permutation robustness means that how much the BSS method is not affected under a certain probability of arising permutation, unlike the conventional permutation-solving approaches. We address to analyze our previously proposed BSS architecture, so called blind spatial subtraction array (BSSA). In BSSA, source extraction is achieved by subtracting the power spectrum of the estimated noise via ICA from the power spectrum of partly speech-enhanced signal via delay-and-sum (DS) procedure. Indeed BSSA partially involves permutation problem in the ICA-based noise estimator part. However, BSSA can efficiently reduce the negative affection of the permutation owing to the over-subtraction in the spectral subtraction and defocusing properties in DS. Experiments using artificial and real-recording-based simulations reveal that the proposed method outperforms the conventional ICA

Blind Spatial Subtraction Array with Independent Component Analysis for Hands-free Speech Recognition

IWAENC2006: the 10th International Work Shop on Acoustic Echo and Noise Control, September 12-14, 2006, Paris, France.In this paper, we propose a new blind spatial subtraction array (BSSA) which contains an accurate noise estimator based on independent component analysis (ICA) to realize a noise-robust hands-free speech recognition. First, a preliminary experiment suggests that the conventional ICA is proficient in the noise estimation rather than the direct speech estimation in real environments, where the target speech can be approximated to a point source but real noises are often not point sources. Secondly, based on the above-mentioned findings, we propose a new noise reduction method which is implemented in subtracting the power pectrum of the estimated noise by ICA from the power spectrum of noise-contaminated observations. This architecture provides us a noise-estimation-error robust speech enhancement which is well applicable to the speech recognition. Finally, the effectiveness of the proposed BSSA is shown in the speech recognition experiment

Blind Speech Extraction Combining ICA-based Noise Estimation and Less-Musical-Noise Nonlinear Post Processing

44th ASILOMAR Conference on Signals, Systems and Computers , November 7-10, 2010, California, USA.In this paper, we propose a new blind speech extraction microphone array combining an independent component analysis (ICA)-based noise estimator and nonlinear signal processing for achieving high-quality speech enhancement. The proposed method consists of three parts, namely, the ICA-based noise estimator for a robust target cancellation, channel-wise spectral subtraction (chSS), and post-beamforming to sum up the chSS outputs. We provide a detailed proof of the less musical noise generation property in the proposed method via higher-order statistics analysis, compared with the conventional multichannel speech enhancement methods. The superiority of the proposed method is assessed in the experimental subjective evaluation