Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize across extremely different training versus test environments. Training and testing were performed using different languages having no known common ancestry and correspondingly large linguistic differences-English for training and Mandarin for testing.

A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions.

Speaker separation is a special case of speech separation, in which the mixture signal comprises two or more speakers. Many talker-independent speaker separation methods have been introduced in recent years to address this problem in anechoic conditions. To consider more realistic environments, this paper investigates talker-independent speaker separation in reverberant conditions.

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

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.

Deep Learning for Talker-dependent Reverberant Speaker Separation: An Empirical Study.

Speaker separation refers to the problem of separating speech signals from a mixture of simultaneous speakers. Previous studies are limited to addressing the speaker separation problem in anechoic conditions. This paper addresses the problem of talker-dependent speaker separation in reverberant conditions, which are characteristic of real-world environments.

A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.

For deep learning based speech segregation to have translational significance as a noise-reduction tool, it must perform in a wide variety of acoustic environments. In the current study, performance was examined when target speech was subjected to interference from a single talker and room reverberation. Conditions were compared in which an algorithm was trained to remove both reverberation and interfering speech, or only interfering speech.

An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker.

Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise.