Markov Models of Telephone Speech Dialogues

Analogue speech signals are the most natural form of communication among humans. The contemporary methods adopted for the analysis of voice transmission by packet switching were designed mainly with respect to a Poisson stream of input packets, for which the probability of an active packet on each input port of the router is a constant value in time. An assumption that is not always valid, since the formation of speech packets during a dialogue is a non-stationary process, in which case mathematical modeling becomes an effective method of analysis, through which necessary estimates of a network node being designed for packet transmission of speech may be obtained. This paper presents the result of analysis of mathematical models of Markov chain based speech packet sources vis-à-vis the peculiarities of telephone dialogue models. The derived models can be employed in the design and development of methods of statistical multiplexing of packet switching network nodes

Development of iSpeak: A voice activated Relationship Management System

A constant source of frustration for subscribers of mobile telephony in Nigeria is the quality of customer care service. The ubiquitous IVR systems deployed by service providers often ends in long and winding texting of digits that terminate in calls to agents with poor CRM attitudes. Automation of most of the functions of the human agent goes a long way in mitigating this problem. This paper describes iSpeak – a system designed to reduce the human–to–human (H2H) interaction in the complaint-lodging and solution provision process to a minimal level where it is not possible to eradicate it totally by a replacement with human–to–system (H2S) interactivity. iSpeak has an inherent capacity for improving the efficiency and drastically cutting CRM cost of corporate organizations. This comes with the attendant advantage of improved business-customer relationship. Keywords – Automatic Speech Recognition, Customer Care Service, Speech-control, Customer Voice Model, Voice Print, Voice Recognition

Towards Understanding Egyptian Arabic Dialogues

Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0308

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems