By Kazumasa Murai, Satoshi Nakamura (auth.), Jhing-Fa Wang, Sadaoki Furui, Biing-Hwang Juang (eds.)
Real international Speech Processing brings jointly in a single position very important contributions and updated examine ends up in this fast-moving region. The members to this paintings have been chosen from the top researchers and practitioners during this box.
The paintings, initially released as quantity 36, Numbers 2-3 of the Journal of VLSI sign Processing structures for sign, photograph, and Video Technology, should be precious to an individual operating or gaining knowledge of within the box of speech processing. It serves as an outstanding reference, supplying perception into the most tough concerns being tested today.
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Extra info for Real World Speech Processing
240 . 200 m This section describes experiments carried out for simultaneous recognition of distant-talking speech in real environments. Figure 17 shows the experiments conditions. The MHT and FSU talkers were located at respective fixed positions at 10 and 170 degrees. The speech data were played back through loudspeakers. 80 cm. The distance between the loudspeakers and the microphone array was 2 m. The reverberation time (T) in the experimental room was 280-ms. Figures 21-23 show the results obtained compared with the results using the image method.
Although high recognition accuracy can be obtained for clean speech using the state-of-the-art technology even if the vocabulary size is large, the accuracy largely decreases in noisy environments. Increasing the robustness to noisy environments is one of the most important issues of ASR. Multi-modal speech recognition, in which acoustic features and other information are used jointly, has been investigated and found to increase robustness and thus improve the accuracy of ASR. Most ofthe multi-modal methods use visual features, typically lip information, in addition to the acoustic features [3-9] .
They tested their method based on pattern matching, and obtained a high performance. In this paper, we propose using the optical-flow analysis for robust audio-visual bimodal speech recognition. Acoustic and visual features are combined in each frame to construct the audio-visual features to be used for model training and recognition. We describe the recognition results of noise-corrupted speech and compare the results with the audio-only recognition method. This paper is organized as follows: In Section 2 the principle of the optical-flow method is explained.