SDVoIP—A Software-Defined VoIP Framework For SIP And Dynamic QoS

Software-defined networking (SDN) allows for the decoupling of the control and data planes, enabling more programmability and a global view of the network. Previous research indicates that traditional applications recreated using SDN principles allow for more granularity and customization. In this research, we extend the insights behind SDN to develop a Voice over Internet Protocol (VoIP) framework with the objective to enhance traditional Session Initiation Protocol (SIP) operation and quality of service (QoS) approaches. The contributions of this research are 2-fold: first, an SIP control application is implemented, which communicates with an SDN controller to provide VoIP call registration and call routing capabilities, thereby eliminating the need for specialized SIP proxy hardware devices; second, a dynamic QoS application is developed that provides the ability to make network-wide QoS decisions based on real-time network measurements of latency, bandwidth and packet loss. Functional validation of the framework is performed to verify its operation. The experiment results indicate that the proposed framework allows for enhancements to traditional QoS implementations.

AN EVALUATION OF SOME FACTORS AFFECTING ACCURACY OF THE VIETNAMESE KEYWORD SPOTTING SYSTEM

Keyword spotting (KWS) is one of the important systems on speech applications, such as data mining, call routing, call center, customer-controlled smartphone, smart home systems with voice control, etc. With the goals of researching some factors affecting the Vietnamese Keyword spotting system, we study the combination architecture of CNN (Convolutional Neural Networks)-RNN (Recurrent Neural Networks) on both clean and noise environments with 2 distance speaker cases: 1m and 2m. The obtained results show that the noise trained models are better performance than clean trained models in any (clean or noise) testing environment. The results in this far-field experiment suggest to us how to choose the suitable distance of the recording microphones to the speaker so that there is no redundancy of data with the contexts considered to be the same.

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.