Cepstral distance based channel selection for distant speech recognition

摘要

Shifting from a single to a multi-microphone setting, distant speech recognition can be benefited from the multiple instances of the same utterance in many ways. An effective approach, especially when microphones are not organized in an array fashion, is given by channel selection (CS), which assumes that for each utterance there is at least one channel that can improve the recognition results when compared to the decoding of the remaining channels. In order to identify this most favourable channel, a possible approach is to estimate the degree of distortion that characterizes each microphone signal. In a reverberant environment, this distortion can vary significantly across microphones, for instance due to the orientation of the speaker's head. In this work, we investigate on the application of cepstral distance as a distortion measure that turns out to be closely related to properties of the room acoustics, such as reverberation time and direct-to-reverberant ratio. From this measure, a blind CS method is derived, which relies on a reference computed by averaging log magnitude spectra of all the microphone signals. Another aim of our study is to propose a novel methodology to analyze CS under a wide set of experimental conditions and setup variations, which depend on the sound source position, its orientation, and the microphone network configuration. Based on the use of prior information, we introduce an informed technique to predict CS performance. Experimental results show both the effectiveness of the proposed blind CS method and the value of the aforementioned analysis methodology. The experiments were conducted using different sets of real and simulated data, the latter ones derived from synthetic and from measured impulse responses. It is demonstrated that the proposed blind CS method is well related to the oracle selection of the best recognized channel. Moreover, our method outperforms a state-of-the-art one, especially on real data.