Speech recognition of double talk using SAFIA-based audio segregation

Toshiyuki Sekiya, Tetsuji Ogawa, Tetsunori Kobayashi

Abstract: Double-talk recognition under a distant microphone condition, a serious problem in speech applications in a real environment, is realized through use of modified SAFIA and acoustic model adaptation or training. The original SAFIA is a high-performance audio segregation method based on band selection using two directivity microphones. We have modified SAFIA by adopting array signal processing and have realized optimal directivity for SAFIA. We also used generalized harmonic analysis (GHA) instead of FFT for the spectral analysis in SAFIA to remove the effect of windowing which causes sound-quality degradation in SAFIA.