Improving a Parametric Model for Speech Quality Assessment in Wireless Communication Systems

Author(s)

Rodriguez, Demostenes Z. ; Pivaro, Gabriel F. ; Rosa, Renata L. ; Mittag, Gabriel ; Moeller, Sebastian ; IEEE

• Document type: Journal article
• Source: 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022); v. N/A, p. 5-pg., 2018-01-01.

Abstract

Telecommunication operators need tools to evaluate and improve the quality of the their services offered. In the case of telephony services, the speech quality perceived by the users is relevant. There are different speech quality assessment methods, one of them is the parametric method that is generally used in network planning. The ITU-T Rec. G107, and its recent updates, is the most representative parametric method. However, it does not consider the impairments occurred in a wireless channel. In this context, we propose the inclusion of parameters, such as fading channel model, signal-to-noise ratio (SNR), and maximum Doppler shift using different modulation schemes. Several wireless communication scenarios were simulated using actual speech samples as input. Then, the impairment speech samples were evaluated by an intrusive signal-based method described in ITU-T Rec. P.862.2, which results are used as ground-truth. Experimental results demonstrated that there is a high correlation between wireless channel parameters and mean opinion score (MOS) index. Thus, an impairment wireless function, named Iw is proposed and added to the E-model wide-band algorithm. Performance validation test results demonstrated that Iw represents a reliable wireless degradation model, reaching a Pearson correlation coefficient (PCC) and a normalized RMSE (NRMSE) of 0:973 and 0:085, respectively. (AU)

• FAPESP's process: 15/24496-0 - Evaluation of the service of communication operators using the voice Quality Index
• Grantee: Demostenes Zegarra Rodriguez
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/25512-0 - Conditional Analysis of Audio and Speech Signals for Coding and Recognition
• Grantee: Miguel Arjona Ramírez
• Support Opportunities: Regular Research Grants