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class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Matus Pleva</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://tuke.academia.edu/">Technical University of Kosice</a>, <a class="u-tcGrayDarker" href="https://tuke.academia.edu/Departments/Department_of_Electronics_and_Multimedia_Telecomunications/Documents">Department of Electronics and Multimedia Telecomunications</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Matus" data-follow-user-id="160433363" data-follow-user-source="profile_button" 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class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114726374/Transition_Relevance_Places_Machine_Learning_Based_Detection_in_Dialogue_Interactions"><img alt="Research paper thumbnail of Transition-Relevance Places Machine Learning-Based Detection in Dialogue Interactions" class="work-thumbnail" src="https://attachments.academia-assets.com/111344869/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726374/Transition_Relevance_Places_Machine_Learning_Based_Detection_in_Dialogue_Interactions">Transition-Relevance Places Machine Learning-Based Detection in Dialogue Interactions</a></div><div class="wp-workCard_item"><span>Elektronika Ir Elektrotechnika</span><span>, Jun 27, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ed15e7e1833de279ccdef9a1b8b9afd3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111344869,"asset_id":114726374,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111344869/download_file?st=MTczMjgyNDUyOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114726374"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item 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The appearance and use of these points in the dialogue ensures a correct and smooth alternation between the speakers. In the presented article, we focused on the study of prosodic speech parameters in the Slovak language, and we tried to experimentally verify the potential of these parameters to detect TRP. To study turn-taking issues in dyadic conversations, the Slovak dialogue corpus was collected and annotated. TRP places were identified by the human annotator in the manual labelling process. The data were then divided into chunks that reflect the length of the interpausal dialogue units and the prosodic features were computed. In the Matlab environment, we compared different types of classifiers based on machine learning in the role of an automatic TRP detector based on pitch and intensity parameters. The achieved results indicate that prosodic parameters can be useful in detecting TRP after splitting the dialogue into interpausal units. 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href="https://www.academia.edu/114726373/A_Preliminary_Study_on_Taiwanese_OCR_for_Assisting_Textual_Database_Construction_from_Historical_Documents"><img alt="Research paper thumbnail of A Preliminary Study on Taiwanese OCR for Assisting Textual Database Construction from Historical Documents" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726373/A_Preliminary_Study_on_Taiwanese_OCR_for_Assisting_Textual_Database_Construction_from_Historical_Documents">A Preliminary Study on Taiwanese OCR for Assisting Textual Database Construction from Historical Documents</a></div><div class="wp-workCard_item"><span>2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP)</span><span>, Dec 11, 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/></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726370/Multimodal_dialogue_system_with_NAO_and_VoiceXML_dialogue_manager">Multimodal dialogue system with NAO and VoiceXML dialogue manager</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The proposed paper describes a multimodal interactive system based on NAO humanoid robot with an ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The proposed paper describes a multimodal interactive system based on NAO humanoid robot with an external dialogue management module VoiceON. 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Designed system enables multimodal interaction with the user in such manner that it takes a speech input from the user and it answers by a combination of synthetic speech and gestures. The core of the system is an external dialogue manager, which interprets VoiceXML language. A pilot speech communication application was designed and a preliminary evaluation was performed using subjective methods. Results of the preliminary evaluation highlights importance of involving gestures into communication exchange. 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href="https://www.academia.edu/114726362/BERT_based_Chinese_Medicine_Named_Entity_Recognition_Model_Applied_to_Medication_Reminder_Dialogue_System"><img alt="Research paper thumbnail of BERT-based Chinese Medicine Named Entity Recognition Model Applied to Medication Reminder Dialogue System" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726362/BERT_based_Chinese_Medicine_Named_Entity_Recognition_Model_Applied_to_Medication_Reminder_Dialogue_System">BERT-based Chinese Medicine Named Entity Recognition Model Applied to Medication Reminder Dialogue System</a></div><div class="wp-workCard_item"><span>2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP)</span><span>, Dec 11, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114726362"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114726362"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114726362; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114726362]").text(description); 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href="https://www.academia.edu/114726356/%E9%81%A9%E5%90%88%E6%BC%B8%E5%87%8D%E4%BA%BA%E4%BD%BF%E7%94%A8%E4%B9%8B%E8%AA%9E%E9%9F%B3%E8%BD%89%E6%8F%9B%E7%B3%BB%E7%B5%B1%E5%88%9D%E6%AD%A5%E7%A0%94%E7%A9%B6_Deep_Neural_Network_Bandwidth_Extension_and_Denoising_Voice_Conversion_System_for_ALS_Patients_"><img alt="Research paper thumbnail of 適合漸凍人使用之語音轉換系統初步研究 (Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients)" class="work-thumbnail" src="https://attachments.academia-assets.com/111344864/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726356/%E9%81%A9%E5%90%88%E6%BC%B8%E5%87%8D%E4%BA%BA%E4%BD%BF%E7%94%A8%E4%B9%8B%E8%AA%9E%E9%9F%B3%E8%BD%89%E6%8F%9B%E7%B3%BB%E7%B5%B1%E5%88%9D%E6%AD%A5%E7%A0%94%E7%A9%B6_Deep_Neural_Network_Bandwidth_Extension_and_Denoising_Voice_Conversion_System_for_ALS_Patients_">適合漸凍人使用之語音轉換系統初步研究 (Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">摘要漸凍人症(肌萎縮性脊隨側索硬化症,Amyotrophic lateral sclerosis, ALS)為一種神經退化性疾病,這種疾病目前還沒有治癒的方法,並會讓漸凍人慢慢失去說話能力,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">摘要漸凍人症(肌萎縮性脊隨側索硬化症,Amyotrophic lateral sclerosis, ALS)為一種神經退化性疾病,這種疾病目前還沒有治癒的方法,並會讓漸凍人慢慢失去說話能力,最終導致無法利用語音與人溝通,而失去自我認同。因此,我們需要為漸凍人建立適合其使用之語音溝通輔具(voice output communication aids, VOCAs),尤其是讓其能具有個人化的合成語音,即病友發病前的聲音,以保持自我。但大部分在 ALS 後期,已經不能講話的病友,都沒有事先妥善保存好個人的錄音,最多只能找出有少量大約 20 分鐘的低品質語音,例如經過失真壓縮(MP3)、只保留低頻寬(8 kHz),或是具有強烈背景雜訊干擾等等,以致無法建構出適合 ALS 病友使用的個人化語音合成系統。針對以上困難,本論文嘗試使用通用語音合成系統搭配語音轉換演算法,並在前級加上語音雜訊消除(speech denoising),後級輔以超展頻模組(speech super-resolution)。以能容忍有背景雜訊的錄音,並能將低頻寬的合成語音加上高頻成分(16 kHz)。以盡量能從低品質語音,重建出接近 ALS 病友原音的高品質合成聲音。其中,speech denoising 使用 WaveNet,speech super-resolution 則利用 U-Net 架構。並先以 20 小時的高品質(棚內錄音)教育電台語料庫,模擬出成對的高雜訊與乾淨語音語句,或是低頻寬與高頻寬語音, The 2019 Conference on Computational Linguistics and Speech Processing ROCLING 2019, pp. 5266 ©The Association for Computational Linguistics and Chinese Language Processing</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="577420730b02fc061abe07f9b1d80f09" class="wp-workCard--action" rel="nofollow" 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ALS)為一種神經退化性疾病,這種疾病目前還沒有治癒的方法,並會讓漸凍人慢慢失去說話能力,最終導致無法利用語音與人溝通,而失去自我認同。因此,我們需要為漸凍人建立適合其使用之語音溝通輔具(voice output communication aids, VOCAs),尤其是讓其能具有個人化的合成語音,即病友發病前的聲音,以保持自我。但大部分在 ALS 後期,已經不能講話的病友,都沒有事先妥善保存好個人的錄音,最多只能找出有少量大約 20 分鐘的低品質語音,例如經過失真壓縮(MP3)、只保留低頻寬(8 kHz),或是具有強烈背景雜訊干擾等等,以致無法建構出適合 ALS 病友使用的個人化語音合成系統。針對以上困難,本論文嘗試使用通用語音合成系統搭配語音轉換演算法,並在前級加上語音雜訊消除(speech denoising),後級輔以超展頻模組(speech super-resolution)。以能容忍有背景雜訊的錄音,並能將低頻寬的合成語音加上高頻成分(16 kHz)。以盡量能從低品質語音,重建出接近 ALS 病友原音的高品質合成聲音。其中,speech denoising 使用 WaveNet,speech super-resolution 則利用 U-Net 架構。並先以 20 小時的高品質(棚內錄音)教育電台語料庫,模擬出成對的高雜訊與乾淨語音語句,或是低頻寬與高頻寬語音, The 2019 Conference on Computational Linguistics and Speech Processing ROCLING 2019, pp. 5266 ©The Association for Computational Linguistics and Chinese Language 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="10445103" id="papers"><div class="js-work-strip profile--work_container" data-work-id="114726374"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114726374/Transition_Relevance_Places_Machine_Learning_Based_Detection_in_Dialogue_Interactions"><img alt="Research paper thumbnail of Transition-Relevance Places Machine Learning-Based Detection in Dialogue Interactions" class="work-thumbnail" src="https://attachments.academia-assets.com/111344869/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726374/Transition_Relevance_Places_Machine_Learning_Based_Detection_in_Dialogue_Interactions">Transition-Relevance Places Machine Learning-Based Detection in Dialogue Interactions</a></div><div class="wp-workCard_item"><span>Elektronika Ir Elektrotechnika</span><span>, Jun 27, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ed15e7e1833de279ccdef9a1b8b9afd3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111344869,"asset_id":114726374,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111344869/download_file?st=MTczMjgyNDUzMCw4LjIyMi4yMDguMTQ2&st=MTczMjgyNDUyOSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114726374"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114726374"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114726374; 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/></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726370/Multimodal_dialogue_system_with_NAO_and_VoiceXML_dialogue_manager">Multimodal dialogue system with NAO and VoiceXML dialogue manager</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The proposed paper describes a multimodal interactive system based on NAO humanoid robot with an ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The proposed paper describes a multimodal interactive system based on NAO humanoid robot with an external dialogue management module VoiceON. System can be controlled by voice. NAO produces speech and gestures as a response to the user inputs. Presented multimodal system uses built-in speech recognition and speech synthesis modules adapted to Slovak language, which was originally not supported. Moreover, system accepts VoiceXML dialogue applications for all supported languages. To manage dialogue interaction, a previously developed VoiceXML dialogue manager was adopted. A new module for generation of multimodal output (speech and gestures) was designed, which enables NAO to produce gestures together with speech in several modes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114726370"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114726370"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114726370; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114726370]").text(description); $(".js-view-count[data-work-id=114726370]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114726370; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114726370']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 114726370, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114726370]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114726370,"title":"Multimodal dialogue system with NAO and VoiceXML dialogue manager","translated_title":"","metadata":{"abstract":"The proposed paper describes a multimodal interactive system based on NAO humanoid robot with an external dialogue management module VoiceON. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="114726368"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/114726368/Preliminary_evaluation_of_the_multimodal_interactive_system_for_NAO_robot"><img alt="Research paper thumbnail of Preliminary evaluation of the multimodal interactive system for NAO robot" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/114726368/Preliminary_evaluation_of_the_multimodal_interactive_system_for_NAO_robot">Preliminary evaluation of the multimodal interactive system for NAO robot</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The proposed paper brings a description of the pilot version of the multimodal dialogue system fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The proposed paper brings a description of the pilot version of the multimodal dialogue system for NAO humanoid robot. Designed system enables multimodal interaction with the user in such manner that it takes a speech input from the user and it answers by a combination of synthetic speech and gestures. The core of the system is an external dialogue manager, which interprets VoiceXML language. A pilot speech communication application was designed and a preliminary evaluation was performed using subjective methods. Results of the preliminary evaluation highlights importance of involving gestures into communication exchange. Moreover the paper brings a discussion of usage scenarios, where designed system can be used for educational purposes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114726368"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114726368"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114726368; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114726368]").text(description); $(".js-view-count[data-work-id=114726368]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114726368; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114726368']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 114726368, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114726368]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114726368,"title":"Preliminary evaluation of the multimodal interactive system for NAO robot","translated_title":"","metadata":{"abstract":"The proposed paper brings a description of the pilot version of the multimodal dialogue system for NAO humanoid robot. 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The novelty of this paper lies in the comparison of performance between timing based and audio based keystroke dynamics data in both an authentication and an identification setting. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings, and tested how well the system could recognize the correct typist. When training with data of 3 sessions and testing with the remaining session we achieved a maximal accuracy of 97.3 % using cross validation. Repeating this with training with 1 session and testing with the 3 remaining sessions we achieved an accuracy of still 90.6 %. The results show the potential of using Audio Keystroke Dynamics information as a way to identify users during log on.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="114726367"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="114726367"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 114726367; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=114726367]").text(description); $(".js-view-count[data-work-id=114726367]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 114726367; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='114726367']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 114726367, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=114726367]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":114726367,"title":"Static Audio Keystroke Dynamics","translated_title":"","metadata":{"abstract":"In this paper we investigate the accuracy of an identification scheme based on the sound of typing a password. 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href="https://www.academia.edu/114726356/%E9%81%A9%E5%90%88%E6%BC%B8%E5%87%8D%E4%BA%BA%E4%BD%BF%E7%94%A8%E4%B9%8B%E8%AA%9E%E9%9F%B3%E8%BD%89%E6%8F%9B%E7%B3%BB%E7%B5%B1%E5%88%9D%E6%AD%A5%E7%A0%94%E7%A9%B6_Deep_Neural_Network_Bandwidth_Extension_and_Denoising_Voice_Conversion_System_for_ALS_Patients_">適合漸凍人使用之語音轉換系統初步研究 (Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">摘要漸凍人症(肌萎縮性脊隨側索硬化症,Amyotrophic lateral sclerosis, ALS)為一種神經退化性疾病,這種疾病目前還沒有治癒的方法,並會讓漸凍人慢慢失去說話能力,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">摘要漸凍人症(肌萎縮性脊隨側索硬化症,Amyotrophic lateral sclerosis, ALS)為一種神經退化性疾病,這種疾病目前還沒有治癒的方法,並會讓漸凍人慢慢失去說話能力,最終導致無法利用語音與人溝通,而失去自我認同。因此,我們需要為漸凍人建立適合其使用之語音溝通輔具(voice output communication aids, VOCAs),尤其是讓其能具有個人化的合成語音,即病友發病前的聲音,以保持自我。但大部分在 ALS 後期,已經不能講話的病友,都沒有事先妥善保存好個人的錄音,最多只能找出有少量大約 20 分鐘的低品質語音,例如經過失真壓縮(MP3)、只保留低頻寬(8 kHz),或是具有強烈背景雜訊干擾等等,以致無法建構出適合 ALS 病友使用的個人化語音合成系統。針對以上困難,本論文嘗試使用通用語音合成系統搭配語音轉換演算法,並在前級加上語音雜訊消除(speech denoising),後級輔以超展頻模組(speech super-resolution)。以能容忍有背景雜訊的錄音,並能將低頻寬的合成語音加上高頻成分(16 kHz)。以盡量能從低品質語音,重建出接近 ALS 病友原音的高品質合成聲音。其中,speech denoising 使用 WaveNet,speech super-resolution 則利用 U-Net 架構。並先以 20 小時的高品質(棚內錄音)教育電台語料庫,模擬出成對的高雜訊與乾淨語音語句,或是低頻寬與高頻寬語音, The 2019 Conference on Computational Linguistics and Speech Processing ROCLING 2019, pp. 5266 ©The Association for Computational Linguistics and Chinese Language Processing</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="577420730b02fc061abe07f9b1d80f09" class="wp-workCard--action" rel="nofollow" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="104938291"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/104938291/Formosa_Speech_Recognition_Challenge_2018_Data_Plan_and_Baselines"><img alt="Research paper thumbnail of Formosa Speech Recognition Challenge 2018: Data, Plan and Baselines" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/104938291/Formosa_Speech_Recognition_Challenge_2018_Data_Plan_and_Baselines">Formosa Speech Recognition Challenge 2018: Data, Plan and Baselines</a></div><div class="wp-workCard_item"><span>2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP)</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper introduces the Formosa speech recognition (FSR) challenge 2018, presents the provided ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper introduces the Formosa speech recognition (FSR) challenge 2018, presents the provided data profile, evaluation plan and reports the experimental results of the baseline systems. This challenge focuses on spontaneous Taiwanese Mandarin speech recognition (TMSR) and it is based on a real-life, multigene broadcast radio speech corpus, NER-Trs-Vol1, selected from the Formosa speech in the wild (FSW) project. To assist participants to establish a good starting system, a set of baseline systems were published based on various deep neural network (DNN) models. NER-Trs-Vol1 is free for participants (noncommercial license), and its corresponding Kaldi recipes for the baselines have been published online. 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