&lt;title&gt;Objective speech quality measures for Internet telephony&lt;/title&gt;

Hall, Timothy A.

doi:10.1117/12.434293

Cited by 15 publications

(12 citation statements)

References 4 publications

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“…For example, impairments due to packet losses have only been explicitly included into the E-Model in the latest revision of the reccomendation (dated from March 2003), and then only a uniform loss distribution is considered, which does not provide an accurate model of packet loss on a big network like the Internet. Therefore, the assessments obtained by means of the E-Model are to be considered in the appropriate context, and it is not surprising that they are not as close to MOS tests as one would want [36]. Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, it is the only available measure which is computationally simple [36] and can be used in real-time applications. However, the E-model was not designed as a quality assessment tool (as clearly stated in the reccomendation defining it), but as a network planning tool.The E-model provides the means to take several quality-affecting parameters into account when designing a telephony network, most of which are more related to the field of signal processing than to that of computer networks.…”

Section: Related Workmentioning

confidence: 99%

“…Fig. 1(a), which is taken from [36], shows a scatter plot for the quality evaluation given by this model with respect to MOS (cf. Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Obviously, the reference is the end-user's perception, which is directly captured by subjective tests. Concerning available objective tests, it is well known that they do not always correlate very well with human perception [36,54]. More details are given in the following paragraphs.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Mohamed

Rubino

Varela

2004

Performance Evaluation

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This paper addresses the problem of quantitatively evaluating the quality of a speech stream transported over the Internet as perceived by the end user. We propose an approach being able to perform this task automatically and, if necessary, in real time. Our method is based on using G-networks (open networks of queues with positive and negative customers) as neural networks (in this case, they are called Random Neural Networks) to learn, in some sense, how humans react vis-a-vis a speech signal that has been distorted by encoding and transmission impairments. This can be used for control purposes, for pricing applications, etc.Our method allows us to study the impact of several source and network parameters on the quality, which appears to be new (previous work analyzes the effect of one or two selected parameters only). In this paper we use our technique to study the impact on performance of several basic source and network parameters on a non-interactive speech flow, namely loss rate, loss distribution, codec, forward error correction, and packetization interval, all at the same time. This is important because speech/audio quality is affected by several parameters whose combined effect is neither well identified nor understood.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Fig. 1(a), which is taken from [36], shows a scatter plot for the quality evaluation given by this model with respect to MOS (cf. Fig.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Mohamed

Rubino

Varela

2004

Performance Evaluation

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“…It has been mentioned that subjective speech quality assessment results are highly accurate and reliable [22][23]. Those methods using subjective methods are mainly divided into four, as shown in Fig 4. Conversational opinion tests require very good control conditions with appropriate manner of test situation, particularly two separated soundproof rooms.…”

Section: Subjective Speech Quality Assessment Methodsmentioning

confidence: 99%

Speech Quality Assessment of VoIP: G.711 VS G.722 Based on Interview Tests with Thai Users

Daengsi¹,

Wutiwiwatchai²,

Preechayasomboon³

et al. 2012

IJITCS

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Abstract-This paper presents the comparison between two codecs, G.711 and G.722 at 64 kbps, referring to speech quality perception using a subjective method called interview tests. These subjective tests have been conducted with 201 subjects, who are Thai native speakers that use Thai which is a tonal language, for accuracy and reliability of results. The results from testing with both codecs are almost the same; the scores are 4.17 for G.722 and 4.14 for G.711. After analyzing the results, it has been confirmed that G.722 does not provide better speech quality than G.711 to the Thai subjects significantly, which is consistent with previous information. However, these results could be used as the benchmark of G.711 and G.722 for speech quality assessment within Thai environments.Index Terms-VoIP, speech quality assessment, speech quality evaluation, speech quality measurement, subjective methods, subjective methods tests, G.711, G.722, Thai

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Evaluating the Utility of Media–Dependent FEC in VoIP Flows

Rubino

Varela

2004

Quality of Service in the Emerging Networking Panorama

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Abstract. In this paper, we present an analysis of the impact of using media-dependent Forward Error Correction (FEC) in VoIP flows over the Internet. This error correction mechanism consists of piggybacking a compressed copy of the contents of packet n in packet n + i (i being variable), so as to mitigate the effect of network losses on the quality of the conversation. To evaluate the impact of this technique on the perceived quality, we propose a simple network model, and study different scenarios to see how the increase in load produced by FEC affects the network state. We then use a pseudo-subjective quality evaluation tool that we have recently developed in order to assess the effects of FEC and the affected network conditions on the quality as perceived by the end-user.

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<title>Objective speech quality measures for Internet telephony</title>

Cited by 15 publications

References 4 publications

Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Performance evaluation of real-time speech through a packet network: a random neural networks-based approach

Speech Quality Assessment of VoIP: G.711 VS G.722 Based on Interview Tests with Thai Users

Evaluating the Utility of Media–Dependent FEC in VoIP Flows

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