IJARAI Volume 4 Issue 2

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: Blocking Black Area Method for Speech Segmentation

Abstract: Speech segmentation is an important sub problem of automatic speech recognition. This research is concerned with the development of a continuous speech segmentation system using Bangla Language. This paper presents a dynamic thresholding algorithm to segment the continuous Bngla speech sentences into words/sub-words. The research uses Otsu’s method for dynamic thresholding and introduces a new approach, named blocking black area method to identify the voiced regions of the continuous speech in speech segmentation. The developed system has been justified with continuously spoken several Bangla sentences. To test the performance of the system, 100 Bangla sentences have been recorded from 5 (five) male speakers of different ages and 656 words have been presented in the 100 Bangla sentences. So, the speech database contains 500 Bangla sentences with 3280 words. All the algorithms and methods used in this research are implemented in MATLAB and the proposed system has been achieved the average segmentation accuracy of 90.58%.

Author 1: Dr. Md. Mijanur Rahman
Author 2: Fatema Khatun
Author 3: Dr. Md. Al-Amin Bhuiyan

Keywords: Blocking Black Area; Boundary Detection; Dynamic Thresholding; Otsu’s Algorithm; Speech Segmentation

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Paper 2: Innovative Processes in Computer Assisted Language Learning

Abstract: Reading ability of an individual is believed to be one of the major sections in language competency. From this perspective, determination of topical writings for second language learners is considered tough exam for language instructor. This mixed i.e. qualitative and quantitative research study aims to address the innovative processes in computer-assisted language learning through surveying the reading level and streamline content of the ESL students in the classrooms designed for students. This study is based on empirical research to measure the reading level among the ESL students. The findings of this study have revealed that using the procedures of language preparing such as shortened text as well as assessed component tools used for automatic text simplification is profitable for both the ESL students and the teachers.

Author 1: Khaled M. Alhawiti

Keywords: Natural Language Processing; Computer Assisted Language Learning; Syntactic Simplification Tools

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Paper 3: Comparative Analysis of Improved Cuckoo Search(ICS) Algorithm and Artificial Bee Colony (ABC) Algorithm on Continuous Optimization Problems

Abstract: This work is related on two well-known algorithm, Improved Cuckoo Search and Artificial Bee Colony Algorithm which are inspired from nature. Improved Cuckoo Search (ICS) algorithm is based on Lévy flight and behavior of some birds and fruit flies and they have some assumptions and each assumption is highly observed to maintain their characteristics. Besides Artificial Bee Colony (ABC) algorithm is based on swarm intelligence, which is based on bee colony with the way the bees maintain their life in that colony. Bees’ characteristics are the main part of this algorithm. This is a theoretical result of this topic and a quantitative research paper.

Author 1: Shariba Islam Tusiy
Author 2: Nasif Shawkat
Author 3: Md. Arman Ahmed
Author 4: Biswajit Panday
Author 5: Nazmus Sakib

Keywords: Artificial Bee Colony (ABC) algorithm; Bioinformatics; Improved Cuckoo Search (ICS) algorithm; Lévy flight; Meta heuristic; Nature Inspired Algorithms

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Paper 4: Speech emotion recognition in emotional feedback for Human-Robot Interaction

Abstract: For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are efficient carriers of emotions. The features of the sound of a spoken voice probably contains crucial information on the emotional state of the speaker, within this framework, a machine might use such properties of sound to recognize emotions. This work evaluated six different kinds of classifiers to predict six basic universal emotions from non-verbal features of human speech. The classification techniques used information from six audio files extracted from the eNTERFACE05 audio-visual emotion database. The information gain from a decision tree was also used in order to choose the most significant speech features, from a set of acoustic features commonly extracted in emotion analysis. The classifiers were evaluated with the proposed features and the features selected by the decision tree. With this feature selection could be observed that each one of compared classifiers increased the global accuracy and the recall. The best performance was obtained with Support Vector Machine and bayesNet.

Author 1: Javier G. R´azuri
Author 2: David Sundgren
Author 3: Rahim Rahmani
Author 4: Aron Larsson
Author 5: Antonio Moran Cardenas
Author 6: Isis Bonet

Keywords: Affective Computing; Detection of Emotional Infor-mation; Machine Learning; Speech Emotion Recognition.

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Paper 5: A Trust-based Mechanism for Avoiding Liars in Referring of Reputation in Multiagent System

Abstract: Trust is considered as the crucial factor for agents in decision making to choose the most trustworthy partner during their interaction in open distributed multiagent systems. Most current trust models are the combination of experience trust and reference trust, in which the reference trust is estimated from the judgements of agents in the community about a given partner. These models are based on the assumption that all agents are reliable when they share their judgements about a given partner to the others. However, these models are no more longer appropriate to applications of multiagent systems, where several concurrent agents may not be ready to share their private judgement about others or may share the wrong data by lying to their partners. In this paper, we introduce a combination model of experience trust and experience trust with a mechanism to enable agents take into account the trustworthiness of referees when they refer their judgement about a given partner. We conduct experiments to evaluate the proposed model in the context of the e-commerce environment. Our research results suggest that it is better to take into account the trustworthiness of referees when they share their judgement about partners. The experimental results also indicate that although there are liars in the multiagent systems, combination trust computation is better than the trust computation based only on the experience trust of agents.

Author 1: Manh Hung Nguyen
Author 2: Dinh Que Tran

Keywords: Multiagent system, Trust, Reputation, Liar.

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