Integration of beamforming and automatic speech recognition through propagation of the wiener posterior
Ramón Fernández Astudillo, Alberto Abad, João Paulo da Silva Neto · 2012
This paper details one of the front-end components of the system used at the PASCAL-CHiME multi-source robust automatic speech recognition (ASR) challenge 2011. The presented approach uses uncertainty propagation techniques to integrate conventional beamforming with automatic speech recognition. The paper addresses the derivation of a complex Gaussian posterior for the multi-channel Wiener and the delay and sum beamformer and introduces a new approach based on the propagation of the Wiener posterior through the resynthesizing process. Results on the PASCAL-CHiME task for this algorithms show that they consistently outperform conventional beamfomers with a minimal increase in computational complexity.