A talker-independent deep learning algorithm to increase intelligibility for hearing-impaired listeners in reverberant competing talker conditions.

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Abstract

Deep learning based speech separation or noise reduction needs to generalize to voices not encountered during training and to operate under multiple corruptions. The current study provides such a demonstration for hearing-impaired (HI) listeners. Sentence intelligibility was assessed under conditions of a single interfering talker and substantial amounts of room reverberation. A talker-independent deep computational auditory scene analysis (CASA) algorithm was employed, in which talkers were separated and dereverberated in each time frame (simultaneous grouping stage), then the separated frames were organized to form two streams (sequential grouping stage). The deep neural networks consisted of specialized convolutional neural networks, one based on U-Net and the other a temporal convolutional network. It was found that every HI (and normal-hearing, NH) listener received algorithm benefit in every condition. Benefit averaged across all conditions ranged from 52 to 76 percentage points for individual HI listeners and averaged 65 points. Further, processed HI intelligibility significantly exceeded unprocessed NH intelligibility. Although the current utterance-based model was not implemented as a real-time system, a perspective on this important issue is provided. It is concluded that deep CASA represents a powerful framework capable of producing large increases in HI intelligibility for potentially any two voices.