TY - GEN
T1 - Evaluation of two simultaneous continuous speech recognition with ICA BSS and MFT-based ASR
AU - Takeda, Ryu
AU - Yamamoto, Shun'ichi
AU - Komatani, Kazunori
AU - Ogata, Tetsuya
AU - Okuno, Hiroshi G.
PY - 2007
Y1 - 2007
N2 - An adaptation of independent component analysis (ICA) and missing feature theory (MFT)-based ASR for two simultaneous continuous speech recognition is described. We have reported on the utility of a system with isolated word recognition, but the performance of the MFT-based ASR is affected by the configuration, such as an acoustic model. The system needs to be evaluated under a more general condition. It first separates the sound sources using ICA. Then, spectral distortion in the separated sounds is estimated to generate missing feature masks (MFMs). Finally, the separated sounds are recognized by MFT-based ASR. We estimate spectral distortion in the temporal-frequency domain in terms of feature vectors, and we generate MFMs. We tested an isolated word and the continuous speech recognition with a cepstral and spectral feature. The resulting system outperformed the baseline robot audition system by 13 and 6 points respectively on the spectral features.
AB - An adaptation of independent component analysis (ICA) and missing feature theory (MFT)-based ASR for two simultaneous continuous speech recognition is described. We have reported on the utility of a system with isolated word recognition, but the performance of the MFT-based ASR is affected by the configuration, such as an acoustic model. The system needs to be evaluated under a more general condition. It first separates the sound sources using ICA. Then, spectral distortion in the separated sounds is estimated to generate missing feature masks (MFMs). Finally, the separated sounds are recognized by MFT-based ASR. We estimate spectral distortion in the temporal-frequency domain in terms of feature vectors, and we generate MFMs. We tested an isolated word and the continuous speech recognition with a cepstral and spectral feature. The resulting system outperformed the baseline robot audition system by 13 and 6 points respectively on the spectral features.
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U2 - 10.1007/978-3-540-73325-6_38
DO - 10.1007/978-3-540-73325-6_38
M3 - Conference contribution
AN - SCOPUS:37349051121
SN - 9783540733225
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 384
EP - 394
BT - New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings
PB - Springer Verlag
T2 - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007
Y2 - 26 June 2007 through 29 June 2007
ER -