Multi-Channel Sub-Band Speech Recognition - McCowan Iain A; Sridharan Sridha (EURASIP Journal on Advances in Signal Processing, 2001)
Two distinct fields of research into robust speech recognition are the use of microphone arrays for signal enhancement and the use of independent frequency sub-band models for robust recognition. In this article, we propose and investigate the integration of these two techniques on two different levels. First, a broad-band beamforming microphone array allows for natural integration with sub-band speech recognition as the beamformer is implemented as a combination of band-limited sub-arrays. Rather than recombining the sub-array outputs to give a single enhanced output, we fuse the output of separate hidden Markov models trained on each sub-array frequency band. Second, a dynamic sub-band weighting algorithm is proposed in which the cross- and auto-spectral densities of the microphone inputs are used to estimate the reliability of each frequency band. The proposed multi-channel sub-band system is evaluated on an isolated digit recognition task and compared to both a standard full-band microphone array system and a single channel sub-band system.