A Novel Uncertainty Parameter SR (Signal To Residual Spectrum Ratio) Evaluation Approach For Speech Enhancement

TL;DR

This paper introduces a new evaluation method called SR (Signal to Residual Spectrum Ratio) for speech enhancement, aiming to improve intelligibility and reduce distortions for hearing-impaired users in non-stationary environments.

Contribution

The paper proposes a novel SR-based evaluation approach that utilizes an uncertainty parameter to better assess speech enhancement quality, especially for hearing aid applications.

Findings

SR and LLR parameters effectively measure attenuation and amplification distortions.

The proposed method outperforms the Weighted Average Technique (WAT) in evaluation.

Segmentation based on multiple thresholds improves noise estimation accuracy.

Abstract

Usually, hearing impaired people use hearing aids which are implemented with speech enhancement algorithms. Estimation of speech and estimation of nose are the components in single channel speech enhancement system. The main objective of any speech enhancement algorithm is estimation of noise power spectrum for non stationary environment. VAD (Voice Activity Detector) is used to identify speech pauses and during these pauses only estimation of noise. MMSE (Minimum Mean Square Error) speech enhancement algorithm did not enhance the intelligibility, quality and listener fatigues are the perceptual aspects of speech. Novel evaluation approach SR (Signal to Residual spectrum ratio) based on uncertainty parameter introduced for the benefits of hearing impaired people in non stationary environments to control distortions. By estimation and updating of noise based on division of original pure signal into three parts such as pure speech, quasi speech and non speech frames based on multiple threshold conditions. Different values of SR and LLR demonstrate the amount of attenuation and amplification distortions. The proposed method will compared with any one method WAT(Weighted Average Technique) Hence by using parameters SR (signal to residual spectrum ratio) and LLR (log like hood ratio), MMSE (Minim Mean Square Error) in terms of segmented SNR and LLR.