Skip to content
Research Article Open access CC BY 4.0

An Improved Logistic Function for Mapping Raw Scores of Perceptual Evaluation of Speech Quality (PESQ)

A. Olatubosun, Patrick O. Olabisi

Journal of Engineering Research and Reports · pp. 1–10 · Published 24 Nov 2018

10.9734/jerr/2018/v3i116704

Abstract

Voice service being the major offering of telecommunication networks, its level of Quality of Service (QoS) largely determines the performance of these networks. This work evaluated the state-of-the-art Perceptual Evaluation of Speech Quality (PESQ) objective model for perceptual estimation of the quality of transmitted speech signals. Perceptual estimation of the quality of speech is predominantly done by subjective techniques and the results presented as Mean Opinion Scores (MOS), which has a scale from 1 for poor quality to 5 for excellent quality. Despite constraints of the subjective approach to perceptual speech quality estimation, its scores serves as the basis for correlating quality scores from objective techniques for speech quality estimation. Original or reference speeches were recorded using professional studio equipment and software, and guided by provisions of ITU-T P.830. The speeches were transmitted over three mobile wireless networks. A speech database consisting of 64 original (32 male and 32 female) and 192 transmitted speeches was developed. Reference speeches and their corresponding transmitted (network-degraded) speeches were tested on the PESQ model to estimate their quality scores. The raw PESQ quality scores are within the scale range of -0.5 and 4.5. They were mapped to the MOS scale for linear comparison of the scales. Study of PESQ model showed several shortcomings, some of which have been improved upon by previous researchers. Evaluating PESQ mapping function (in ITU-T Rec P.862.1) showed the need for better coverage of the MOS scale. Analysis of solution for the logistic growth function was done and parameters were optimised which resulted in the development of a new robust logistic mapping function. The raw PESQ quality scores were mapped using the developed mapping function as well as two known standard mapping functions, namely: ITU-T P.862.1 and Morfitt and Cotanis mapping functions. The mapped scores known as PESQ MOS-listening quality objective (PESQ MOS-LQO) obtained with the three functions were tested using ANOVA at a significant figure of . The developed logistic mapping function offered a quality score coverage of 98.6% of the MOS scale. This was evaluated against the two known standard mapping functions and the developed function offered improvement of 11.8 and 4.9% over and above their 86.8 and 93.7% coverage of the MOS scale respectively. At the significance level of , an F-value of 60.6042, a critical-F of 3.04, and a p-value of 4.61721E-21 were obtained. With p < 0.05, the Null Hypothesis was rejected, and the critical-F value being less than the F-statistic value confirmed the rejection. Therefore, the data distribution of at least one of the functions has a different mean and belongs to a separate population of performance.

Mapping speech quality logistic functions perceptual models sigmoid symmetry

Cited by 13

Corn: Co-Trained Full- and No-Reference Speech Quality Assessment

Pranay Manocha, Donald S. Williamson, Adam Finkelstein · IEEE International Conference on Acoustics, Speech, and Signal Processing · 2023

Efficient Speech Quality Assessment Using Self-Supervised Framewise Embeddings

Karl El Hajal, Zihan Wu, Neil Scheidwasser · IEEE International Conference on Acoustics, Speech, and Signal Processing · 2022

Diffusion-Based Generative Speech Source Separation

Robin Scheibler, Youna Ji, Soo-Whan Chung · IEEE International Conference on Acoustics, Speech, and Signal Processing · 2022

A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids

Ruxue Guo, Ruiyu Liang, Qingyun Wang · Applied Sciences · 2022

Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features

Ryandhimas E. Zezario, Szu-Wei Fu, Fei Chen · IEEE/ACM Transactions on Audio Speech and Language Processing · 2021

A QoE Test System for Vehicular Voice Cloud Services

Kailiang Zhang, Lei Chen, Yuan An · Journal on spesial topics in mobile networks and applications · 2019

Bitrate and Tandem Detection for the AMR-WB Codec with Application to Network Testing

Tobias Hübschen, G. Schmidt · European Signal Processing Conference · 2018

Training and compensation of class-conditioned NMF bases for speech enhancement

H. Chung, R. Badeau, É. Plourde · Neurocomputing · 2018

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

13

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.