Browsing by Author "Pulasinghe, K"

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Adapting MaryTTS for Synthesizing Sinhalese Speech to Communicate with Children
(IEEE, 2021-12-01) Lakmal, M. A. J. A; Methmini, K. A. D. G; Rupasinghe, D. M. H. M; Hettiarachchi, D. I; Piyawardana, V; Senarathna, M; Reyal, S; Pulasinghe, K
The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1–6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM -based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented.
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ASD Screening for Toddlers via Physical Interpretation through Advanced AI
(IEEE, 2021-12-09) Jayasekera, D; Alwis, H; Dissanayaka, H; Mudalinayake, R; Piyawardana, V; Pulasinghe, K
Autism Spectrum Disorders (ASD) are generally causing challenges for significant communication, social interaction, and behavioral patterns to elderly people and children. Providing early treatments can make a huge advancement in the lives of children. Meanwhile, there is a limited number of systems to screen and identify ASD children. This research project is about developing a set of tools bonding together to one system called "AI - Bot Simon" to screen kids with ASD by filling the gap. In the system development process mainly, Audio, Facial expressions, Gestures, and the Gates of a targeted group of children are considered for screening. Since the target group is 6 months to 4 years, they are in early language development age. On the technical side of view Machine Learning (ML) and Deep Learning (DL) with Neural Networks (NN) are used for advanced screening and monitoring for automation of the process. In the last step of the development, all the outputs or information gathered from each tool or model, processed, analyzed, and provided to the users of the system by an Artificial Intelligence (AI) bot implemented with a web application and a mobile application whether children are suffering from ASD or not.
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Automated question answering for customer helpdesk applications
(IEEE, 2011-08-16) Samarakoon, L.; Kumarawadu, S; Pulasinghe, K
This paper describes a closed domain question answering system which can be used as the first step in automating a customer helpdesk of a commercial organization. Even though there has been an increasing interest in data-driven methods over the past decade to achieve more natural humanmachine interactions, such methods require a large amount of manually labeled representative data on how user converses with a machine. However, this is a strong requirement that is difficult to be satisfied in the early phase of system development. The knowledge-based approach that we present here is aimed at maximally making use of the user experience available with the customer services representatives (CSRs) in the organization and hence not relying on application data. The approach takes into account the syntactic, lexical, and morphological variations, as well as a way of synonym transduction that is allowed to vary over the systems knowledgebase. The query understanding method, which is based on a ranking algorithm and a pattern writing process, takes into account the intent, context, and content components of natural language meaning as well as the word order. A genetic algorithm-based method is presented for regularly updating the ranking parameters to adapt to changes in the nature of users’ queries over time. We present an evaluation of our system deployed in a real-world enterprise helpdesk environment at Exetel Pty Ltd., Australia.
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Automated Sinhala Speech Emotions Analysis Tool for Autism Children
(IEEE, 2021-08-11) Welarathna, K. T; Kulasekara, V; Pulasinghe, K; Piyawardana, V
— Autism Spectrum Disorder (ASD) is a neurological disorder that impairs children's development and symptoms that can be noticed in early childhood. One of the main diagnosis characteristics of ASD is the child having unusual emotions and expressions during social interactions. The main problem is how to distinguish these symptoms. Only 14 out of 100 Autistic kids, before they reach the age of 24 months, get medical treatments since the unavailability of resources to identify them early. If they can be recognized early, a therapeutic engagement can be done to help them overcome those issues in social interactions, when they reach school-going age. The focus of this research is to develop a tool to screen atypical children from typical children. This research attempts to recognize the correct emotion of a child, while the child is talking. The input audio stream of children was normalized into a specific range, sub-framed into 2s length for language-independent, noise reduction, and age independence features, and extracting the most effective 40 audio features. The Convolutional Neural Network (CNN) based model classifies eight different emotions of sad, disgust, surprise, neutral, happy, calm, fear, and angry with an accuracy matrix of F1 score of 0.90, even in the uncontrol environment. If the classifying emotions have small frequency variances, the trained model has the ability to handle them.
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Comparision Between Features of CbO based Algorithms for Generating Formal Concepts
(IGI Global, 2016-01-01) Kodagoda, N; Pulasinghe, K
Formal Concept Analysis provides the mathematical notations for representing concepts and concept hierarchies making use of order and lattice theory. This has now been used in numerous applications which include software engineering, linguistics, sociology, information sciences, information technology, genetics, biology and in engineering. The algorithms derived from Kustenskov's CbO were found to provide the most efficient means of computing formal concepts in several research papers. In this paper key enhancements to the original CbO algorithms are discussed in detail. The effects of these key features are presented in both isolation and combination. Eight different variations of the CbO algorithms highlighting the key features were compared in a level playing field by presenting them using the same notation and implementing them from the notation in the same way. The three main enhancements considered are the partial closure with incremental closure of intents, inherited canonicity test failures and using a combined depth first and breadth first search. The algorithms were implemented in an un-optimized way to focus on the comparison on the algorithms themselves and not on any efficiencies provided by optimizing code. The main contribution of this paper is the complete comparison of the three main enhancements used in recent variations of the CbO based algorithms. The main findings were that there is a significant performance improvement partial closure with incremental closure of intents is used in isolation. However, there is no significant performance improvement when the depth and breadth first search or the inherited canonicity test failure feature is used in isolation. The inherited canonicity test failure needs to be combined with the combined depth and breadth first feature to obtain a performance increase. Combining all the three enhancements brought the best performance.
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Comparision Between Features of CbO based Algorithms for Generating Formal Concepts
(IGI Global, 2016-01-01) Kodagoda, N; Pulasinghe, K
Formal Concept Analysis provides the mathematical notations for representing concepts and concept hierarchies making use of order and lattice theory. This has now been used in numerous applications which include software engineering, linguistics, sociology, information sciences, information technology, genetics, biology and in engineering. The algorithms derived from Kustenskov's CbO were found to provide the most efficient means of computing formal concepts in several research papers. In this paper key enhancements to the original CbO algorithms are discussed in detail. The effects of these key features are presented in both isolation and combination. Eight different variations of the CbO algorithms highlighting the key features were compared in a level playing field by presenting them using the same notation and implementing them from the notation in the same way. The three main enhancements considered are the partial closure with incremental closure of intents, inherited canonicity test failures and using a combined depth first and breadth first search. The algorithms were implemented in an un-optimized way to focus on the comparison on the algorithms themselves and not on any efficiencies provided by optimizing code. The main contribution of this paper is the complete comparison of the three main enhancements used in recent variations of the CbO based algorithms. The main findings were that there is a significant performance improvement partial closure with incremental closure of intents is used in isolation. However, there is no significant performance improvement when the depth and breadth first search or the inherited canonicity test failure feature is used in isolation. The inherited canonicity test failure needs to be combined with the combined depth and breadth first feature to obtain a performance increase. Combining all the three enhancements brought the best performance.
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Control of redundant manipulators by fuzzy linguistic commands
(IEEE, 2003-08-04) Pulasinghe, K; Watanabe, K; Izumi, K; Kiguchi, K
This paper presents a method of controlling redundant manipulator by spoken language commands consisting fuzzy linguistic information. The present system introduces the fuzzyneuro control paradigm to the contemporary speech controlled robotic systems, which are based on on-off control paradigm. The system is sensitive to the action activation commands, action modification commands, and action repetition commands of the human-robot conversation carried out by practical dialogues. C.redibility of the proposed system is experimentally proved by controlling a manipulator with seven degrees-of-freedom by fuzzy linguistic information enriched spoken language commands to perform an assembling task.
Open Access
A Fuzzy-Neural Network Based Human-Machine Interface for Voice Controlled Robots Trained by a Particle Swarm Optimization
(Korean Institute of Intelligent Systems, 2003-09-25) Watanabe, K; Chatterjee, A; Pulasinghe, K; Izumi, K; Kiguchi, K
Particle swarm optimization (PSO) is employed to train fuzzy-neural networks (FNN), which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. The system has been successfully employed in a real life situation for navigation of a mobile robot.
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iPillow: Sleep Quality Improvement System
(IEEE, 2019-12-05) Amarasena, J; Indimagedara, N; Wanigasuriya, S; Bandare, B; Pulasinghe, K; Tharmaseelan, J
A quality sleep is essential for the maintenance of the internal organs, to improve memory and brain functionalities, reduce the stress, and to improve our health state. We focused on “Accurate sleep stages detection methods and sleep quality improvement methods” to implement a device that improves the quality of sleep. This device will have an EEG (electroencephalogram) sensor along with the heartbeat, gyroscope and pressure sensors to increase the accuracy of the sleep level identification and a medication system to play binaural beats to trigger the sleep. Furthermore, a survey will be conducted in the public to gather the data on sleeping disorders and performing an analysis using an algorithm on gathered information, a sleep support health mobile application will be designed to review user's daily sleep quality and share health tips daily according to the user's health situation. As the final product, we hope to invent a smart pillow called “IPillow” which is capable of improving the sleep quality.
Open Access
Knowledge Management for Effective Clinical Diagnosis in Developing Countries
(Journal of Information Technology Review, 2013-05-02) Amararachchi, J. L; Pulasinghe, K; Perera, H. S. C
In the last two decades, the Information and Communication Technologies (ICTs) revolution has redefined the structure of the 21st century healthcare organization. The fundamental challenge faced by the 21st century clinical practitioner in a developing country is to acquire proficiency in understanding and interpreting clinical information so as to update knowledge that leverage the quality of decisions made at the clinics. An additional challenge must be considered by the clinical practitioners to make potentially life-saving decisions whilst attempting to deal with large amounts of clinical data. Since the Clinical Knowledge Management Systems (CKMS) consist of most related Data, Information and Knowledge, it could be utilized to achieve the above challenges. Shortage of medical experts in Health Institutions located in rural and remote areas in developing countries being a huge problem which effects badly to the quality of healthcare. By providing facilities for medical practitioners to access KMS, this problem can be alleviated substantially. A Knowledge Management (KM) solution would allow healthcare institutions to give clinical data context, so as to allow knowledge derivation for more effective clinical diagnosis. It would also provide a mechanism for effective transfer of the acquired knowledge in order to aid healthcare workers as and when required. This study has identified the factors that affect to the knowledge management initiatives. There is a strong association between accessing and using Information/knowledge in clinical activities and quality of healthcare. Moreover, attitudes of Medical Practitioners (MP), Infrastructure facilities, patient Information systems, patient treatment, staff benefits etc., have shown positive effect to the success of Knowledge Management in Health Institutions. The research has used a case study methodology for accomplishing the research objectives. Rural and remote areas in Sri Lanka have been considered for the case study since it is one of the developing countries situated in the Asian region. Based on the outcome of the study, we introduce a KM framework for Healthcare Institutions which would assist HIs to discover and create new knowledge. The framework has been validated using a sample of 15 hospitals situated in the Kandy district in Sri Lanka.
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Knowledge management framework for achieving quality of healthcare in the developing countries
(IEEE, 2013-01-20) Amararachchi, J. L; Perera, H. S. C; Pulasinghe, K
A severe dearth of medical experts in health institutions in the rural and remote areas in developing countries has directly affected the quality of healthcare. This problem can be alleviated by providing facilities to access up to date medical Information and knowledge for doctors who are stationed in these areas to update their knowledge. Since Knowledge Management System (KMS) consists of most related Information and knowledge, medical KMSs could be utilized to enhance the quality of clinical activities. This study was aimed to identify the factors that affect the knowledge management initiatives. Findings of the research have shown that there is a strong association between accessing and using Information/ knowledge in clinical activities and the quality of healthcare. Moreover, attitudes of Medical Practitioners (MP), Infrastructure facilities, patient Information systems, patient treatment, staff benefits etc., have contribute positively towards the success of knowledge management in Health organizations. The research has used the case study methodology for accomplishing the research objectives. Remote and rural areas in Sri Lanka have considered for the case study which is one of the developing countries in the Asian region.
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Knowledge management framework for achieving quality of healthcare in the developing countries
(IEEE, 2013-01-20) Amararachchi, J. L; Perera, H. S. C; Pulasinghe, K
A severe dearth of medical experts in health institutions in the rural and remote areas in developing countries has directly affected the quality of healthcare. This problem can be alleviated by providing facilities to access up to date medical Information and knowledge for doctors who are stationed in these areas to update their knowledge. Since Knowledge Management System (KMS) consists of most related Information and knowledge, medical KMSs could be utilized to enhance the quality of clinical activities. This study was aimed to identify the factors that affect the knowledge management initiatives. Findings of the research have shown that there is a strong association between accessing and using Information/ knowledge in clinical activities and the quality of healthcare. Moreover, attitudes of Medical Practitioners (MP), Infrastructure facilities, patient Information systems, patient treatment, staff benefits etc., have contribute positively towards the success of knowledge management in Health organizations. The research has used the case study methodology for accomplishing the research objectives. Remote and rural areas in Sri Lanka have considered for the case study which is one of the developing countries in the Asian region.
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Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children
(IEEE, 2019-10-24) Wijesinghe, A; Samarasinghe, P; Seneviratne, S; Yogarajah, P; Pulasinghe, K
Children with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.
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Machine learning based automated speech dialog analysis of autistic children
(IEEE, 2019-10-24) Wijesinghe, A; Samarasinghe, P; Seneviratne, S; Yogarajah, P; Pulasinghe, K
Children with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.
Open Access
Mobile office and its implications for ERP systems: a review of literature
(International Journal of Advanced Research in Computer Science and Software Engineering, 2015-05-05) Nawaz, S. S; Pulasinghe, K; Thelijjagoda, S
The surprisinggrowth of mobile technology has seen many benefits. During the recent decades businesses also try to benefit from this; one of them is mobile office / enterprise. When organizations become mobile-enabled, their ERP systems also face some implications which have to be carefully dealt with. The implementation of mobileoffice forces organizations to reengineer some or all of its business processes, etc. If all opportunities offered by technologyare carefully acquired, organizations should be prospering for sure.
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Modular fuzzy-neuro controller driven by spoken language commands
(IEEE, 2004-01-30) Pulasinghe, K; Watanabe, K; Izumi, K; Kiguchi, K
We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands. The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands. Significance of the words, i.e., the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire. Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system. The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands.
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A novel modular neuro-fuzzy controller driven by natural language commands
(IEEE, 2001-07-27) Watanabe, K; Pulasinghe, K; Kiguchi, K; Izumi, K
A method of interpreting imprecise natural language commands to machine understandable manner is presented in this paper. The proposed method tries to ease the process of man-machine interaction by combining the theoretical understanding of artificial neural networks and fuzzy logic. Both fields are very popular to mimic the human behavior in different research areas in artificial intelligence. The proposed system tries to understand the natural language command rather than mere recognition. The distinctive features of the artificial neural networks in pattern recognition and classification and the abilities of manipulating imprecise data by fuzzy systems are merged to recognize the machine sensitive words in the natural language command and then to interpret them to machine in machine identifiable manner. Modularity of the design tries to break up the complete task into manageable parts where the presence of individual part is vital to bridge the so-called man-machine gap.
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A parallel version of the in-close algorithm
(IEEE, 2017-01-27) Kodagoda, N; Andrews, S; Pulasinghe, K
This research paper presents a new parallel algorithm for computing the formal concepts in a formal context. The proposed shared memory parallel algorithm Parallel-Task-In-Close3 parallelizes Andrews's In-Close3 serial algorithm. The paper presents the key parallelization strategy used and presents experimental results of the parallelization using the OpenMP framework.
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A parallel version of the in-close algorithm
(IEEE, 2017-01-27) Pulasinghe, K; Kodagoda, N; Andrews, S
This research paper presents a new parallel algorithm for computing the formal concepts in a formal context. The proposed shared memory parallel algorithm Parallel-Task-In-Close3 parallelizes Andrews's In-Close3 serial algorithm. The paper presents the key parallelization strategy used and presents experimental results of the parallelization using the OpenMP framework.
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A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems
(IEEE, 2005-12-05) Chatterjee, A; Pulasinghe, K; Watanabe, K; Izumi, K
This paper shows the possible development of particle swarm optimization (PSO)-based fuzzy-neural networks (FNNs) that can be employed as an important building block in real robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs that can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by a user. The FNN is also trained to capture the user-spoken directive in the context of the present performance of the robot system. Hidden Markov model (HMM)-based automatic speech recognizers (ASRs) are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system has been successfully employed in two real-life situations, namely: 1) for navigation of a mobile robot; and 2) for motion control of a redundant manipulator.