The appearance of Voice over IP (VoIP) revolutionized the telecommunications word, this technology delivers voice communications over the internet protocol (IP) networks instead of the public switched telephone network (PSTN), calls can be made between two VoIP phones as well as between a VoIP phone and an analog phone connected to a VoIP adapter [1]. The use of this technology gives access to more communication options compared to the conventional telephony but the users face different problems, mostly connectivity and performance issues related to different factors such as latency and jitter [2], these factors affect directly the call quality and can result in choppy voice, echoes, or even in a call failure. The main objective of this work was to create a tool for automatic analysis and evaluation from packet traces, identify connectivity and performance issues, reconstruct the audio streams and estimate the call quality. The results of this work showed that the objectives sated above are met, where a tool that automatically analyzes VoIP calls is created, this tool takes non encrypted pcap files as input and returns a list of calls with different parameters related to connectivity and performance such as delay and jitter, it does as well reconstruct the audio of every VoIP stream and plots the waveform and spectrum of the reconstructed audio for evaluation purposes.

The emerging popularity and interest in Voice-over-IP (VoIP) has been accompanied by customer concerns about voice quality over these networks. The lack of an appropriate real-time capable infrastructure in packet networks along with the threats of denial-of service (DoS) attacks can deteriorate the service that these voice calls receive. And these conditions contribute to the decline in call quality in VoIP applications; therefore, error-correcting/concealing techniques remain the only alternative to provide a reasonable protection for VoIP calls against packet losses. Traditionally, each voice call employs its own end-to-end forward-error-correction (FEC) mechanisms. In this paper, we show that when VoIP calls are aggregated over a provider's link, with a suitable linear-time encoding for the aggregated voice traffic, considerable quality improvement can be achieved with little redundancy. We show that it is possible to achieve rates closer to channel capacity as more calls are combined with very small output loss rates even in the presence of significant packet loss rates in the network. The advantages of the proposed scheme far exceed similar or other coding techniques applied to individual voice calls.

Yes / Every contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is characterised by subjectivity, which in turn creates a skewed picture of agent performance. Detecting and eliminating subjectivity is the study challenge that requires empirical research to address. In this paper, we introduce an evidence-based machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using a Deep Neural Network (DNN) for a multi-classification problem. The study draws the first baseline for subjectivity detection, achieving an accuracy of 75%, which is close to relevant speech studies in emotional recognition and performance classification. Among other findings, we conclude that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process, or subjective scores should be deducted from the overall results.

Call quality has been crucial from the inception of telecommunication networks. Operators need to monitor call quality from the end-user's perspective, in order to retain subscribers and reduce subscriber 'churn'. Operators worry not only about call quality and interconnect revenue loss, but also about network connectivity issues in areas where mobile network gateways are prevalent. Bandwidth quality as experienced by the end-user is equally important in helping operators to reduce churn. The parameters that network operators use to improve call quality are mainly from the end-user's perspective. These parameters are usually ASR (answer seizure ratio), PDD (postdial delay), NER (network efficiency ratio), the number of calls for which these parameters have been analyzed and successful calls. Operators use these parameters to evaluate and optimize the network to meet their quality requirements. Analysis of speech quality is a major arena for research. Traditionally, users' perception of speech quality has been measured offline using subjective listening tests. Such tests are, however, slow, tedious and costly. An alternative method is therefore needed; one that can be automatically computed on the subscriber's handset, be available to the operator as well as to subscribers and, at the same time, provide results that are comparable with conventional subjective scores. QMeter® 'a set of tools for signal and bandwidth measurement that have been developed bearing in mind all the parameters that influence call and bandwidth quality experienced by the end-user' addresses these issues and, additionally, facilitates dynamic tariff propositions which enhance the credibility of the operator. This research focuses on call quality parameters from the end-user's perspective. The call parameters used in the research are signal strength, successful call rate, normal drop call rate, and hand-over drop rate. Signal strength is measured for every five milliseconds of an active call and average signal strength is calculated for each successful call. The successful call rate, normal drop rate and hand-over drop rate are used to achieve a measurement of the overall call quality. Call quality with respect to bundles of 10 calls is proposed. An attempt is made to visualize these parameters for better understanding of where the quality is bad, good and excellent. This will help operators, as well as user groups, to measure quality and coverage. Operators boast about their bandwidth but in reality, to know the locations where speed has to be improved, they need a tool that can effectively measure speed from the end-user's perspective. BM (bandwidth meter), a tool developed as a part of this research, measures the average speed of data sessions and stores the information for analysis at different locations. To address issues of quality in the subscriber segment, this research proposes the varying of tariffs based on call and bandwidth quality. Call charging based on call quality as perceived by the end-user is proposed, both to satisfy subscribers and help operators to improve customer satisfaction and increase average revenue per user. Tariff redemption procedures are put forward for bundles of 10 calls and 10 data sessions. In addition to the varying of tariffs, quality escalation processes are proposed. Deploying such tools on selected or random samples of users will result in substantial improvement in user loyalty which, in turn, will bring operational and economic advantages.

Call quality has been crucial from the inception of telecommunication networks. Operators need to monitor call quality from the end-user¿s perspective, in order to retain subscribers and reduce subscriber ¿churn¿. Operators worry not only about call quality and interconnect revenue loss, but also about network connectivity issues in areas where mobile network gateways are prevalent. Bandwidth quality as experienced by the end-user is equally important in helping operators to reduce churn. The parameters that network operators use to improve call quality are mainly from the end-user¿s perspective. These parameters are usually ASR (answer seizure ratio), PDD (postdial delay), NER (network efficiency ratio), the number of calls for which these parameters have been analyzed and successful calls. Operators use these parameters to evaluate and optimize the network to meet their quality requirements. Analysis of speech quality is a major arena for research. Traditionally, users¿ perception of speech quality has been measured offline using subjective listening tests. Such tests are, however, slow, tedious and costly. An alternative method is therefore needed; one that can be automatically computed on the subscriber¿s handset, be available to the operator as well as to subscribers and, at the same time, provide results that are comparable with conventional subjective scores. QMeter® ¿ a set of tools for signal and bandwidth measurement that have been developed bearing in mind all the parameters that influence call and bandwidth quality experienced by the end-user ¿ addresses these issues and, additionally, facilitates dynamic tariff propositions which enhance the credibility of the operator. This research focuses on call quality parameters from the end-user¿s perspective. The call parameters used in the research are signal strength, successful call rate, normal drop call rate, and hand-over drop rate. Signal strength is measured for every five milliseconds of an active call and average signal strength is calculated for each successful call. The successful call rate, normal drop rate and hand-over drop rate are used to achieve a measurement of the overall call quality. Call quality with respect to bundles of 10 calls is proposed. An attempt is made to visualize these parameters for better understanding of where the quality is bad, good and excellent. This will help operators, as well as user groups, to measure quality and coverage. Operators boast about their bandwidth but in reality, to know the locations where speed has to be improved, they need a tool that can effectively measure speed from the end-user¿s perspective. BM (bandwidth meter), a tool developed as a part of this research, measures the average speed of data sessions and stores the information for analysis at different locations. To address issues of quality in the subscriber segment, this research proposes the varying of tariffs based on call and bandwidth quality. Call charging based on call quality as perceived by the end-user is proposed, both to satisfy subscribers and help operators to improve customer satisfaction and increase average revenue per user. Tariff redemption procedures are put forward for bundles of 10 calls and 10 data sessions. In addition to the varying of tariffs, quality escalation processes are proposed. Deploying such tools on selected or random samples of users will result in substantial improvement in user loyalty which, in turn, will bring operational and economic advantages.