000192385 001__ 192385
000192385 005__ 20190316235757.0
000192385 037__ $$aREP_WORK
000192385 088__ $$aIdiap-RR-40-2011
000192385 245__ $$aIMPROVING MICROPHONE ARRAY SPEECH RECOGNITION WITH COCHLEAR IMPLANT-LIKE SPECTRALLY REDUCED SPEECH
000192385 269__ $$a2011
000192385 260__ $$bIdiap$$c2011
000192385 336__ $$aReports
000192385 520__ $$aCochlear implant-like spectrally reduced speech (SRS) has previously been shown to afford robustness to additive noise. In this paper, it is evaluated in the context of microphone array based automatic speech recognition (ASR). It is compared to and combined with post-filter and cepstral normalisation techniques. When there is no overlapping speech, the combination of cepstral normalization and the SRS-based ASR framework gives a performance comparable with the best obtained with a non-SRS baseline system, using maximum a posteriori (MAP) adaptation, either on microphone array signal or lapel microphone signal. When there is overlapping speech from competing speakers, the same combination gives significantly better word error rates compared to the best ones obtained with the previously published baseline system. Experiments are performed with the MONC database and HTK toolkit.
000192385 700__ $$aDo, Cong-Thanh
000192385 700__ $$aTaghizadeh, Mohammad J.
000192385 700__ $$aGarner, Philip N.
000192385 8564_ $$uhttps://infoscience.epfl.ch/record/192385/files/Do_Idiap-RR-40-2011.pdf$$zn/a$$s627990$$yn/a
000192385 909C0 $$xU10381$$0252189$$pLIDIAP
000192385 909CO $$ooai:infoscience.tind.io:192385$$qGLOBAL_SET$$pSTI$$preport
000192385 937__ $$aEPFL-REPORT-192385
000192385 970__ $$aDo_Idiap-RR-40-2011/LIDIAP
000192385 973__ $$aEPFL
000192385 980__ $$aREPORT