Work

Evaluation of Speaker-Conditioned Target Speaker Extraction Algorithms for Hearing-Impaired Listeners

Year: 2025

Type: article

Abstract: Speaker-conditioned target speaker extraction algorithms aim at extracting the target speaker from a mixture of multiple speakers by using additional information about the target speaker. Previous studies have evaluated the performance of these algorithms using either instrumental measures or subjective assessments with normal-hearing listeners or with hearing-impaired listeners. Notably, a previous study employing a quasicausal algorithm reported significant intelligibility improvements for both normal-hearing and hearing-impaired listeners, while another study demonstrated that a fully causal algorithm could enhance speech intelligibility and reduce listening effort for normal-hearing listeners. Building on these findings, this study focuses on an in-depth subjective assessment of two fully causal deep neural network-based speaker-conditioned target speaker extraction algorithms with hearing-impaired listeners, both without hearing loss compensation (unaided) and with linear hearing loss compensation (aided). Three different subjective performance measurement methods were used to cover a broad range of listening conditions, namely paired comparison, speech recognition thresholds, and categorically scaled perceived listening effort. The subjective evaluation results with 15 hearing-impaired listeners showed that one algorithm significantly reduced listening effort and improved intelligibility compared to unprocessed stimuli and the other algorithm. The data also suggest that hearing-impaired listeners experience a greater benefit in terms of listening effort (for both male and female interfering speakers) and speech recognition thresholds, especially in the presence of female interfering speakers than normal-hearing listeners, and that hearing loss compensation (linear amplification) is not required to obtain an algorithm benefit.

Source: Trends in Hearing

DOI: 10.1177/23312165251365802

Authors: Ragini Sinha, Ann-Christin Scherer, Simon Doclo, Christian Rollwage, Jan Rennies

Institutions: Fraunhofer Institute for Digital Media Technology, Carl von Ossietzky Universität Oldenburg, Hearing4all

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,650