Improving performance in noise for hearing aids and cochlear implants using coherent modulation filtering

Abstract

This study evaluated the maximal attainable performance of speech enhancement strategies based on coherent modulation filtering. An optimal adaptive coherent modulation filtering algorithm was designed to enhance known signals from a target talker in two-talker babble noise. The algorithm was evaluated in a closed-set, speech-recognition-in-noise task. The speech reception threshold (SRT) was measured using a one-down, one-up adaptive procedure. Five hearing-impaired subjects and five cochlear implant users were tested in three processing conditions: (1) original sounds; (2) fixed coherent modulation filtered sounds; and (3) optimal coherent modulation filtered sounds. Six normal-hearing subjects were tested with a 6-channel cochlear implant simulation of sounds processed in the same three conditions. Significant improvements in SRTs were observed when the signal was processed with the optimal coherent modulation filtering algorithm. There was no benefit when the signal was processed with the fixed modulation filter. The current study suggested that coherent modulation filtering might be a promising method for front-end processing in hearing aids and cochlear implants. An approach such as hidden Markov models could be used to generalize the optimal coherent modulation filtering algorithm to unknown utterances and to extend it to open-set speech.