Non‑intrusive speech quality assessment using context‑aware neural networks
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Speech Technology
Abstract
To meet the human perceived quality of experience (QoE) while communicating over various Voice over Internet protocol
(VoIP) applications, for example Google Meet, Microsoft Skype, Apple FaceTime, etc. a precise speech quality assessment
metric is needed. The metric should be able to detect and segregate different types of noise degradations present in the surroundings
before measuring and monitoring the quality of speech in real-time. Our research is motivated by the lack of clear
evidence presenting speech quality metric that can firstly distinguish different types of noise degradations before providing
speech quality prediction decision. To that end, this paper presents a novel non-intrusive speech quality assessment metric
using context-aware neural networks in which the noise class (context) of the degraded or noisy speech signal is first identified
using a classifier then deep neutral networks (DNNs) based speech quality metrics (SQMs) are trained and optimized
for each noise class to obtain the noise class-specific (context-specific) optimized speech quality predictions (MOS scores).
The noisy speech signals, that is, clean speech signals degraded by different types of background noises are taken from the
NOIZEUS speech corpus. Results demonstrate that even in the presence of less number of speech samples available from the
NOIZEUS speech corpus, the proposed metric outperforms in different contexts compared to the metric where the contexts
are not classified before speech quality prediction.
Description
Keywords
Non-intrusive · Speech quality · Speech enhancement · Voice activity detector · Artificial neural network · Quality of experience