Measuring and Monitoring Speech Quality for Voice over IP with POLQA, ViSQOL and P.563

There are many types of degradation which can occur in Voice over IP (VoIP) calls. Of interest in this work are degradations which occur independently of the codec, hardware or network in use. Specifically, their effect on the subjective and objec- tive quality of the speech is examined. Since no dataset suit- able for this purpose exists, a new dataset (TCD-VoIP) has been created and has been made publicly available. The dataset con- tains speech clips suffering from a range of common call qual- ity degradations, as well as a set of subjective opinion scores on the clips from 24 listeners. The performances of three ob- jective quality metrics: POLQA, ViSQOL and P.563, have been evaluated using the dataset. The results show that full reference metrics are capable of accurately predicting a variety of com- mon VoIP degradations. They also highlight the outstanding need for a wideband, single-ended, no-reference metric to mon- itor accurately speech quality for degradations common in VoIP scenarios

Non-intrusive speech quality assessment using context-aware neural networks

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.publishedVersio