SPQER: Speech Quality Evaluation Using Word Recognition for VoIP Communication in Lossy and Mobile Networks

Autor(en): Schuetz, Bertram
Aschenbruck, Nils 
Stichwörter: Computer Science; Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic; Lossy networks; machine learning; quality of service; voice over IP
Erscheinungsdatum: 2020
Herausgeber: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Journal: IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY
Volumen: 1
Startseite: 145
Seitenende: 154
Zusammenfassung: 
In this paper, we introduce SPQER (pronounced speaker), a novel approach to evaluate the quality of experience for real-time Voice over IP (VoIP) communication in mobile and lossy networks. Traditional speech quality metrics, e.g., Perceptual Evaluation of Speech Quality (PESQ) or the Hearing-Aid Speech Quality Index (HASQI), directly compare frequencies and amplitudes to calculate the received signal distortions. SPQER instead uses machine learning classification to evaluate the percentage of recognizable words in conjunction with a time-based decay function to penalize delay and cross-talking. So instead of evaluating noise, SPQER directly answers the question: What percentage of words is the recipient able to understand? We presented a sensitivity analysis, which is based on testbed experiments for different packet loss rates and simulated delays, to asses the impact of challenging link conditions. A final correlation analysis to a short user study shows that SPQER can better evaluate the amount of understandable words than PESQ and HASQI, while still giving a more precise indication about the voice quality than the Word Error Rate (WER) metric.
DOI: 10.1109/OJCS.2020.3011392

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