Beamforming for Distant Speech Recognition in Reverberant Environments and Double-Talk Scenarios

Result of the Month


Beamforming is crucial for distant-speech recognition to mitigate causes of system degradation, e.g., interfering noise sources or competing speakers. We introduced adaptations of state-of-the-art broadband data-independent and data-dependent beamformers to uniform circular arrays (UCA), such that competing speakers are attenuated sufficiently for distant speech recognition.

Contact: Hannes Pessentheiner

The newly introduced multiple null synthesis robust least squares frequency invariant beamformer (MNS-RLSFI) is a data-independent beamformer which enables null-placement in the directions of competing speakers. It is based on convex optimization methods that determine the weighting coefficients. The figure illustrates the directivity pattern of the MNS-RLSFI based on a 24-element UCA, a steering direction of 0 degrees, and a localized competing speaker at 45 degrees.

Our experiments show that data-independent beamformers feature a better performance than data-dependent beamformers in case of double-talk scenarios in reverberant environments. According to our results, the delay-and-sum beamformer is the most robust beamformer which exhibits the highest improvements in real-data scenarios, whereas the MNS-RLSFI outperforms all other beamformers in case of simulated free-field scenarios.

More information can be found in the Master Thesis.

1. June 2012 - 30. June 2012