Spectral-change enhancement with prior SNR for the hearing impaired

TL;DR

This study enhances spectral-change algorithms for hearing-impaired listeners by using prior and estimated SNRs to improve speech intelligibility and naturalness in noisy environments, with promising results.

Contribution

It introduces SNR-based manipulation of spectral-change enhancement, improving speech recognition and quality for hearing-impaired users in noisy conditions.

Findings

SCE-iSNR improved speech intelligibility at high SMRs.

SCE-eSNR enhanced naturalness and speech quality.

Both algorithms showed benefits in different noise scenarios.

Abstract

A previous signal processing algorithm that aimed to enhance spectral changes (SCE) over time showed benefit for hearing-impaired (HI) listeners to recognize speech in background noise. In this work, the previous SCE was manipulated to perform on target-dominant segments, rather than treating all frames equally. Instantaneous signal-to-noise ratios (SNRs) were calculated to determine whether the segments should be processed. Initially, the ideal SNR calculated by the knowledge of premixed signals was introduced to the previous SCE algorithm (SCE-iSNR). Speech intelligibility (SI) and clarity preference were measured for 12 HI listeners in steady speech-spectrum noise (SSN) and six-talk speech (STS) maskers, respectively. The results showed the SCE-iSNR algorithm improved SI significantly for both maskers at high signal-to-masker ratios (SMRs) and for STS masker at low SMRs, while processing effect on speech quality was small. Secondly, the estimated SNR obtained from real mixtures was used, resulting in another SCE-eSNR. SI and subjective rating on naturalness and speech quality were tested for 7 HI subjects. The SCE-eSNR algorithm showed improved SI for SSN masker at high SMRs and for STS masker at low SMRs, as well as better naturalness and speech quality for STS masker. The limitations of applying the algorithms are discussed.