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Proceedings ArticleDOI

Design and collection of acoustic sound data for hands-free speech recognition and sound scene understanding

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TLDR
Progress of the sound scene database collection project and application to environment sound recognition and hands-free speech recognition are described.
Abstract: 
The sound data for open evaluation is necessary for studies such as sound source localization, sound retrieval, sound recognition and hands-free speech recognition in real acoustic environments. This paper reports on our project for acoustic data collection. There are many kinds of sound scenes in real environments. The sound scene is specified by sound sources and room acoustics. The number of combinations of the sound sources, source positions and rooms is huge in real acoustic environments. We assumed that the sound in the environments can be simulated by convolution of the isolated sound sources and impulse responses. As an isolated sound source, hundred kinds of environment sounds and speech sounds are collected. The impulse responses are collected in various acoustic environments. Additionally we collected sounds from a moving source. In this paper, progress of our sound scene database collection project and application to environment sound recognition and hands-free speech recognition are described.

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References
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Proceedings Article

Acoustical Sound Database in Real Environments for Sound Scene Understanding and Hands-Free Speech Recognition

TL;DR: LREC2000: the 2nd International Conference on Language Resources and Evaluation, May 31 - June 2, 2000, Athens, Greece.
Proceedings Article

Data Collection in Real Acoustical Environments for Sound Scene Understanding and Hands-Free Speech Recognition

TL;DR: EUROSPEECH1999: the 6th European Conference on Speech Communication and Techinology, September 5-9, 1999, Budapest, Hungary.
Proceedings Article

Use of different microphone array configurations for hands-free speech recognition in noisy and reverberant environment.

TL;DR: In this work hands-free continuous speech recognition based on microphone arrays is investigated and HMM adaptation was used to realign the recognizer acoustic modeling to the given acoustic condition.
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