QoE Estimation Method for Interconnected VoIP Networks Employing Different Codecs

Takahashi, Akihiro; Egi, Noritsugu; Kurashima, Atsuko

doi:10.1093/ietcom/e90-b.12.3572

Cited by 5 publications

(5 citation statements)

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“…It has been widely used in the recent few years to evaluate and develop parametric speech quality models over a wide range of packet-based network impairments [4,14,15,16,22,23,24]. Figure 3 gives the basic components of a software-based SQA framework which aims at modeling of the listening speech quality according to a set of signal-and packet-layer measurements.…”

Section: Framework For Speech Quality Modelingmentioning

confidence: 99%

“…However, the large number of conditions makes large scale subjective testing unreasonable in terms of cost and time. That is why, full-reference signal-layer objective approaches, which give a tight estimation of subjective scores, are used to measure the perceptual quality [13,14,15,16].…”

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confidence: 99%

“…That is why the E-Model has been adapted and upgraded by several researchers to be able to predict on call-by-call basis the perceptual quality of VoIP conversations [15,16,19,20,21,22]. Accordingly, extended E-Model can act as a single-ended packet-layer parametric SQM tool of VoIP conversations.…”

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confidence: 99%

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Single-ended parametric voicing-aware models for live assessment of packetized VoIP conversations

et al. 2010

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The perceptual quality of VoIP conversations depends tightly on the pattern of packet losses, i.e., the distribution and duration of packet loss runs. The wider (resp. smaller) the inter-loss gap (resp. loss gap) duration, the lower is the quality degradation. Moreover, a set of speech sequences impaired using an identical packet loss pattern results in a different degree of perceptual quality degradation because dropped voice packets have unequal impact on the perceived quality. Therefore, we consider the voicing feature of speech wave included in lost packets in addition to packet loss pattern to estimate speech quality scores. We distinguish between voiced, unvoiced, and silence packets. This enables to achieve better correlation and accuracy between human-based subjective and machine-calculated objective scores. This paper proposes novel no-reference parametric speech quality estimate models which account for the voicing feature of signal wave included in missing packets. Precisely, we develop separate speech quality estimate models, which capture the perceptual effect of removed voiced or unvoiced packets, using elaborated simple and multiple regression analyses. A new speech quality estimate model, which mixes voiced and unvoiced quality scores to compute the overall speech quality score at the end of an assessment interval, is developed following a rigorous multiple linear regression analysis. The input parameters of proposed voicingaware speech quality estimate models, namely Packet Loss Ratio (PLR) and Effective Burstiness Probability (EBP), are extracted based on a novel Markov model of voicing-aware packet loss which captures properly the feature of packet loss process as well as the voicing property of speech wave included in lost packets. The conceived voicing-aware packet loss model is calibrated at run time using an efficient packet loss event driven algorithm. The performance evaluation study shows that our voicing-aware speech quality estimate models outperform voicing-unaware speech quality estimate models, especially in terms of accuracy over a wide range 2 of conditions. Moreover, it validates the accuracy of the developed parametric no-reference speech quality models. In fact, we found that predicted scores using our speech quality models achieve an excellent correlation with measured scores (>0.95) and a small mean absolute deviation (<0.25) for ITU-T G.729 and G.711 speech CODECs.

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Section: Framework For Speech Quality Modelingmentioning

confidence: 99%

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confidence: 99%

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Single-ended parametric voicing-aware models for live assessment of packetized VoIP conversations

et al. 2010

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“…Once the optimal-correlated transformations for all potential parameters are obtained, the overall speech quality estimate model, which combines potential transformed parameters, is derived following a multiple linear regression analysis (see Figure 3) [12]. To do E n = :t(x(m)w(n -m)Y (3) m=n -N+l…”

Section: Iiiframework For Vocal Quality Modelingmentioning

confidence: 99%

“…In fact, this category of speech assessment algorithms estimates PQoS of live VoIP conversations without intrusion at run-time. The parametric feature of such speech quality measurement 978-1-4244-4624-7/09/$25.00 ©2009 IEEE algorithm enables to quantify PQoS of VoIP conversations using a set of gathered measures form the header content of voice packet stream [1,2,3]. This particular property enables the deployment of such a vocal quality measurement tool in intermediate-or terminal-node.…”

Section: Introductionmentioning

confidence: 99%

Voicing-aware parametric speech quality models over VoIP networks

Jelassi

Youssef

Hoene

et al. 2009

2009 Global Information Infrastructure Symposium

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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.

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A Web Script-Based Method to Evaluate User Satisfaction of Mobile Services in the Field

Uemura

Niida

Nakamura

2010

2010 Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services

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QoE Estimation Method for Interconnected VoIP Networks Employing Different Codecs

Cited by 5 publications

References 0 publications

Single-ended parametric voicing-aware models for live assessment of packetized VoIP conversations

Single-ended parametric voicing-aware models for live assessment of packetized VoIP conversations

Voicing-aware parametric speech quality models over VoIP networks

A Web Script-Based Method to Evaluate User Satisfaction of Mobile Services in the Field

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