Research Publications

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Author: search.filters.author.Pulasinghe, KHas files: Yes

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Now showing 1 - 10 of 38
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    Adaptive Robotic Voice Modulation for ASD Kids: Tailored Voice Pitch, Tone, and Speed
    (Institute of Electrical and Electronics Engineers Inc., 2025) Panduwawala, P; Pulasinghe, K; Rajapaksha, S
    Children with Autism Spectrum Disorder (ASD) often experience sensory sensitivities, particularly auditory hypersensitivity, which can make interactions and communication challenging. This study explores the customization of the NAO robot's voice pitch, tone, and speech speed using the Kaldi Speech Recognition Toolkit to align with the preferences of children with ASD. Eight distinct voice profiles were created, offering a range of variations in pitch, tone, and speech speed. Parents or caretakers were asked to select the voice profile they felt would be most suitable for their child. Based on this feedback, we created a spectrum of voices tailored to each child's needs. Results indicate that medium-pitch and moderate-speed combinations are most effective in enhancing engagement, with Voice 2 emerging as the preferred profile. The findings underscore the potential of adaptive voice modulation in improving robotic interactions for ASD therapy and highlight opportunities for further research in real-time adaptability and long-term impact assessment.
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    Designing Culturally Adaptive Emotional Gestures to Enhance Child-Robot Interaction with NAO Robots in ASD Therapy
    (Institute of Electrical and Electronics Engineers Inc., 2025) Manukalpa, C.S; Pulasinghe, K; Rajapakshe, S
    Integrating social robots into human-robot interactions demands advancements in natural language processing, navigation, computer vision, and expressive gestures to foster meaningful interactions. However, a gap remains in designing culturally relevant and developmentally appropriate gestures, particularly in the Sri Lankan context. Autism Spectrum Disorder (ASD), a neurodevelopmental condition impacting early education, often remains underdiagnosed, exacerbating learning challenges. This study introduces a novel approach utilizing robot-child interactions for ASD screening to minimize such delays. Expressive gestures were developed for the NAO6 humanoid robot to engage Sinhala-speaking children aged 2 to 6 years, including those with ASD, in Sri Lanka. Using the NAOqi Python API and Choregraphe simulator, culturally aligned gestures expressing emotions like happiness, sadness, fear, anger, and more were designed and synchronized with voice and LED effects. Pilot studies with typical children demonstrated the significance of linguistic and cultural alignment in enhancing engagement, emotional response, and trust. By addressing cultural nuances and advancing early ASD screening, this framework holds potential for broader applications in education, therapy, and diagnosis, improving human-robot interactions globally.
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    Child's Age Range Prediction Using Sinhala Speech Recognition System
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kathriarachchi, A; Pulasinghe, K
    This study predicts the age range of a child speaking Sinhala by analyzing voice characteristics and acoustic features. Identifying speech impairments in children aged 6 to 72 months is critical for early intervention, mainly when using a system that recognizes their native language. The developed system generates accurate insights to assist speech pathologists in diagnosing speech disorders. A Multilayer Perceptron neural network is proposed for age group prediction, leveraging Mel Frequency Cepstral Coefficients (MFCC) and pitch features to enhance recognition accuracy. The system demonstrated an overall accuracy rate of 77% in age range identification, providing a valuable tool for healthcare professionals to evaluate and monitor speech development in Sinhala-speaking children
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    Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System
    (Institute of Electrical and Electronics Engineers Inc., 2025) Jayawardena, A; Pulasinghe, K; Rajapakshe, S
    This paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog Management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.
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    Step-by-Step Process of Building Voices for Under Resourced Languages using MARY TTS Platform
    (IEEE, 2022-12-09) Senarathna, M; Pulasinghe, K; Reyal, S
    This paper presents a comprehensive guide for creating synthetic voices to support under resourced languages for the MaryTTS platform. Although researchers have extensively contributed in the domain of speech synthesis, the lack of a thorough documentation hinders the voice building process for languages not yet supported by MaryTTS, complicating the implementation process for users with inadequate knowledge in the field of Text-to-Speech (TTS). The step-by-step process discussed in this study is further demonstrated with the creation of a synthetic voice for the Sinhala language, with unit selection as the voice building approach. A Sinhalese voice was generated with an intelligibility score of 91.7% upon evaluation with Diagnostic Rhyme Test (DRT). Comparison with ground truth data proved a close approximation to human speech where the intelligibility score was identified as 97.9%, when tested with the same participants. The Mean Opinion Score (MOS) revealed a naturalness level of 2.993, indicating a moderately high speech quality for the proposed system in comparison with the ideal score of 4.972.
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    SeizeIT: SEIZURE victims are no longer leashed
    (Institute of Advanced Engineering and Science, 2019-09-18) Wimalarathne, M. A. J. I; Ubeysingha, K. U. K; Imbulana, I. A. D. M; Welikala, W. A. D. R; Pulasinghe, K
    Seizure is considered to be one of the severe and most common type of neurological disorders. Despite the availability of numerous anti-seizure drugs, it is often difficult to control the disease completely and effectively. Lack of close supervision and failure in providing urgent medical care during and after seizure episodes, leads to serious injuries or even death. On the other hand, the use of wireless sensor networks in everyday applications have rapidly increased due to decreased technology costs and improved product reliability. Therefore, developing a wearable device to monitor seizure may complete the anamnesis, help medical staff in diagnosing and acute treatment while preventing seizure related accidents. There are number of seizure detection systems available in the market. Still their performance is far from perfect. This paper explores an application of biomedical wireless sensor networks, which attempts to monitor patients in a completely non-invasive and non-intrusive manner. It describes a wearable device together with seizure prediction and alerting system, which is designed to address some issues with seizure detection systems in the market. Its functional block diagram and operating modes are detailed. Possible application areas of the device are also discussed.
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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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    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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    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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    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.