Abstract: The fifth generation of mobile networks is expected to include Internet of Things (IoT) as an important innovative kind of accessibility. Various Machine to Machine (M2M) services will be provided through this type of connectivity. Requirements of these services are various mainly in term of security and quality of service. Some of these services, such as fleet management, are time sensitive and generate a considerable amount of signaling messages. In this paper, we propose an innovative framework in order to optimize the quality of service for fleet management M2M communications through the reduction the signaling overhead.

• Publication Date: 16-Dec-2016
• DOI: 10.15224/978-1-63248-113-9-49
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Abstract: In some languages like Classical Arabic (The language of the Holy Quran), phoneme duration is considered as a distinguishing cue in Quranic phonology. Phonological variation of phonemes occurrences contributes to an inaccurate pronunciation of phonemes and therefore inaccurate ASR system. Thus a good phonemes duration modeling can be an essential issue. Currently, the most effective models used in automatic speech recognition (ASR) systems are based on statistical approaches namely Hidden Markov Model (HMM). In standard HMM speech recognition framework, the duration information is poorly employed. However, previous studies have demonstrated that using an HMM with explicit duration modeling techniques have improved the recognition performances in many targeted languages. This paper presents an important phase of our ongoing work which aims to build an accurate Arabic recognizer for teaching and learning purposes. It presents an implementation of an HsMM model (Hidden semi-Markov Model)

• Publication Date: 16-Dec-2016
• DOI: 10.15224/978-1-63248-113-9-53
• Views: 0
• Downloads: 0