Abstract
Automatic Speech Recognition (ASR) in reverberant rooms can be improved by choosing training data from the same acoustical environment as the test data. In a real-world application this is often not possible. A solution for this problem is to use speech signals from a close-talking microphone and reverberate them artificially with multiple room impulse responses. This paper shows results on recognizers whose training data differ in size and percentage of reverberated signals in order to find the best combination for data sets with different degrees of reverberation. The average error rate on a close-talking and a distant-talking test set could thus be reduced by 29% relative.
Our work was partially supported by the German Federal Ministry of Education and Research (grant no. 01 IMD 01 F) in the frame of the SmartWeb project. The responsibility for the contents of this study lies with the authors.
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Haderlein, T., Nöth, E., Herbordt, W., Kellermann, W., Niemann, H. (2005). Using Artificially Reverberated Training Data in Distant-Talking ASR. In: MatouÅ¡ek, V., Mautner, P., Pavelka, T. (eds) Text, Speech and Dialogue. TSD 2005. Lecture Notes in Computer Science(), vol 3658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551874_29
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DOI: https://doi.org/10.1007/11551874_29
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