International audienceThis survey gives a comprehensive review of recent advances related to the topic of VoIP QoE (Quality of user' Experience). It starts by providing some insight into the QoE arena and outlines the principal building blocks of a VoIP application. The sources of impairments over data IP networks are identified and distinguished from signal-oriented sources of quality degradation observed over telecom networks. An overview of existing subjective and objective methodologies for the assessment of the QoE of voice conversations is then presented outlining how subjective and objective speech quality methodologies have evolved to consider time-varying QoS transport networks. A description of practical procedures for measuring VoIP QoE and illustrative results is then given. Utilization methodology of several speech quality assessment frameworks is summarized. A survey of emerging single-ended parametric-model speech quality assessment algorithms dedicated to VoIP service is then given. In particular, after presenting a primitive single-ended parametric-model algorithm especially conceived for the evaluation of VoIP conversations, new artificial assessors of VoIP service are detailed. In particular, we describe speech quality assessment algorithms that consider, among others, packet loss burstiness, unequal importance of speech wave, and transient loss of connectivity. The following section concentrates on the integration of VoIP service over mobile data networks. The impact of quality-affecting phenomena, such as handovers and CODEC changeover are enumerated and some primary subjective results are summarized. The survey concludes with a review of open issues relating to automatic assessment of VoIP

A revolutionary feature of emerging media services over the Internet is their ability to account for human perception during service delivery processes, which surely increases their popularity and incomes. In such a situation, it is necessary to understand the users' perception, what should obviously be done using standardized subjective experiences. However, it is also important to develop artificial quality assessors that enable to automatically quantify the perceived quality. This efficiently helps performing optimal network and service management at the core and edges of the delivery systems. In our article, we explore the behavior rating of new emerging artificial speech quality assessors of VoIP calls subject to moderately bursty packet loss processes. The examined Speech Quality Assessment (SQA) algorithms are able to estimate speech quality of live VoIP calls at runtime using control information extracted from header content of received packets. They are especially designed to be sensitive to packet loss burstiness. The performance evaluation study is performed using a dedicated set-up software-based SQA framework. It offers a specialized packet killer and includes the implementation of four SQA algorithms. A speech quality database, which covers a wide range of bursty packet loss conditions, has been created and then thoroughly analyzed. Our main findings are the following: (1) all examined automatic bursty-loss aware speech quality assessors achieve a satisfactory correlation under upper (> 20%) and lower (< 10%) ranges of packet loss processes; (2) they exhibit a clear weakness to assess speech quality under a moderated packet loss process; (3) the accuracy of sequence-by-sequence basis of examined SQA algorithms should be addressed in detail for further precision.

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This paper describes novel parametric speech quality models which subsume the effect of packet loss distribution and voicing feature of missing signal waves. Speech quality estimate models for voiced and unvoiced loss location patterns are developed following multiple statistical regression analysis of measurements gathered from a built speech quality assessment framework. The overall speech quality is estimated by combining voiced and unvoiced speech quality estimate scores using an expression calibrated using a large number of speech samples. The input parameters namely, mean loss durations and ratios for voiced and unvoiced packets, of speech quality estimate models are extracted at run-time using a new voicing-aware packet loss Markov model. This chain, calibrated at run-time, finely models bursty packet loss behavior over voiced and unvoiced missing speech waves. Performance evaluation study shows that our voicing-aware speech quality estimate models clearly outperform voicing-agnostic speech quality models in terms of accuracy over a wide range of conditions.