Binary and ratio time-frequency masks for robust speech recognition

A time-varying Wiener filter specifies the ratio of a target signal and a noisy mixture in a local time-frequency unit. We estimate this ratio using a binaural processor and derive a ratio time-frequency mask. This mask is used to extract the speech signal, which is then fed to a conventional speech recognizer operating in the cepstral domain. We compare the performance of this system with a missing-data recognizer that operates in the spectral domain using the time-frequency units that are dominated by speech. To apply the missing-data recognizer, the same binaural processor is used to estimate an ideal binary time-frequency mask, which selects a local time-frequency unit if the speech signal within the unit is stronger than the interference. We find that the performance of the missing data recognizer is better on a small vocabulary recognition task but the performance of the conventional recognizer is substantially better when the vocabulary size is increased.