This paper proposes a deep learning-based non-intrusive objective speech intelligibility estimation method based on recurrent neural network (RNN) with long short-term memory (LSTM) structure. Conventional non-intrusive estimation methods such as standard P.563 have poor estimation performance and lack of consistency, especially, in various noise and reverberation environments. The proposed method trains the LSTM RNN model parameters by utilizing the STOI that is the standard intrusive intelligibility estimation method with reference speech signal. The input and output of the LSTM RNN are the MFCC vector and the frame-wise STOI value, respectively. Experimental results show that the proposed objective intelligibility estimation method outperforms the conventional standard P.563 in various noisy and reverberant environments.

With the increasing demand for IP telephony services using Voice over IP (VoIP) technology, techniques for monitoring speech quality in actual networks are required to manage the quality of VoIP services constantly. Since the speech quality of VoIP is affected by IP network performance factors, non-intrusive methods of monitoring the quality of service (QoS) by passively measuring network performance are being watched with keen interest. VQmon technology is one of the non-intrusive quality monitoring methods. Although the monitoring functions of the VQmon for post-arrived packet behavior events at VoIP-gateways are effective, the estimating algorithm does not take differences in the implementations of VoIP-gateway products into account. We therefore propose a non-intrusive method of monitoring QoS that works in conjunction with ITU-T Recommendation P.862 "PESQ" that takes the characteristics of VoIP-gateway products into consideration. We compared the performance of non-intrusive quality monitoring technology such as VQmon and the proposed method in terms of estimating the accuracy of speech quality and mouth-to-ear delay. The experimental results revealed that the proposed method outperforms the conventional one, achieving sufficient accuracy for quality monitoring of VoIP services.

Network applications such as FTP, WWW, Mirroring etc. are presently operated with little or no knowledge about the characteristics of the underlying network. These applications could operate more efficiently if the characteristics of the network are known and/or are made available to the concerned application. But network characteristics are hard to come by. The IP Performance Metrics working group (IETF-IPPM-WG) is working on developing a set of metrics that will characterize Internet data delivery services (networks). Some tools are being developed for measurements of these metrics. These generally involve active measurements or require modificationsin applications. Both techniques have their drawbacks. In this work, we show a new and more practical approach of estimating network characteristics. This involves gathering and analyzing the network's experience. The experience is in the form of traffic statistics, information distilled from management related activities and ubiquitously available logs (squid access logs, mail logs, ftp logs etc. ) of network applications. An analysis of this experience provides an estimate of the characteristics of the underlying network. To evaluate the concept we have developed and experimented with a system wherein the network characteristics are generated by analyzing the logs and traffic statistics. The network characteristics are made available to network clients and administrators by Network Performance Metric (NPM) servers. These servers are accessed using standard network management protocols. Results of the evaluation are presented and a framework for efficient operation of network operations, using the network characteristics is outlined.