- K. Kumatani, J. McDonough, B. Raj. None. Fundamental Technologies in Modern Speech Recognition Distant Speech Recognition Holds the Promise of the Most Natural Human Computer Interface Because It Enables Man-machine Interactions through Speech. Abstract: D istant speech recognition (DSR) holds the promise of the most natural human computer interface because it enables man-machine interactions through speech, without the necessity of donning intrusive body-or head-mounted microphones. Recognizing distant speech robustly, however, remains a challenge. This contribution provides a tutorial overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beamforming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Moreover, we present an emerging technology in the area of far-field audio and speech processing based on spherical microphone arrays. Performance comparisons of spherical and linear arrays reveal that a spherical array with a diameter of 8.4 cm can provide recognition accuracy comparable or better than that obtained with a large linear array with an aperture length of 126 cm. INTRODUCTION When the signals from the individual sensors of a microphone array with a known geometry are suitably combined, the array [ From close-talking microphones to far-field sensors ] functions as a spatial filter capable of suppressing noise, reverberation, and competing speech. Such beamforming techniques have received a great deal of attention within the acoustic array processing community in the recent past [1]– [7]. Despite this effort, however, such techniques have often been ignored within the mainstream community working on DSR. As pointed out in [6] and [7], this could be due to the fact that the disparate research communities for acoustic array processing and automatic speech recognition (ASR) have failed to adopt each other's best practices. For instance, the array processing community tends to ignore speaker adaptation techniques, which can compensate for mismatches between acoustic conditions during training and testing. Moreover, this community has largely preferred to work on controlled, synthetic recordings, obtained by convolving noise-and reverberation-free speech with measured, static room impulse responses, with subsequent artificial addition of which featured actual array recordings of real speakers; this task, however , has fallen out of favor, to the extent that it is currently not even mentioned on the PASCAL CHiME Challenge Web site, nor in any of the concomitant publications. This is unfortunate because improvements obtained with novel speech enhancement techniques tend to diminish, or …
