Speech Recognition in Multisource Reverberant Environments with Binaural Inputs

We present a binaural solution to robust speech recognition in multi-source reverberant environments. We employ the notion of an ideal time-frequency binary mask, which selects the target if it is stronger than the interference in a local time-frequency (T-F) unit. Our system estimates this ideal binary mask at the output of a target cancellation module implemented using adaptive filtering. This mask is used in conjunction with a missing-data algorithm to decode the target utterance. A systematic evaluation in terms of automatic speech recognition (ASR) performance shows substantial improvements over the baseline performance and better results over related two-microphone approaches