The Experts below are selected from a list of 153 Experts worldwide ranked by ideXlab platform

Marius Cristian Cerlinca - One of the best experts on this subject based on the ideXlab platform.

  • Developing aLogopaedic Mobile Device UsinganFPGA
    2020
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe
    Abstract:

    Inthis paper wewill describe adevice developed for * thedimensions ofthefinal device should besmall children withspeech Disorders caused bybadpronunciation or enough tobeusedevenbysmall children; evenbybadhearing ofcertain phonemes. First, wewilldefine * tohavearechargeable battery. speechFluency andspeechFluency Disorder. SpeechFluency designates theeasyandconfidant construction ofaphonic stream thatisunderstandable tothereceiver. Speech Fluency Disorder III. WHY FPGA? points totheelements whichsignificantly impair theconstruction Inorder toanswer thisquestion, wehavetodetermine the ofthephonic stream andwhichmakeunderstanding bythe hardware requirements forourmobiledevice: receiver difficult (apartial definition, illocutionary connector in * amicroprocessor (faster isbetter); theformofametalingual commentary). Speech Fluency Disorder, * amicrophone andaspeaker; sodefined, isaninherent quality ofstuttering 111. * aADC forrecording audiosamples andaDAC for ~~~~~~~~~~~there weren't PDAswithADC/DACconverters and, second, small children dowiththis device should besimilar with t w simple gamesthat sometimes give bonuses totheplayer. we needsome realtime responses fromthedevice, whichis Themainidea behind theconcept ofamobile device for notthecasewithPDAoperating system. children withspeech Disorder isthat, sometimes, they can Eventually, we hadtochoose fromtheother twopossible interact better with alifeless device thanwith ahumanbeingsolutions. We choose theFPGAbecause we candesign/use a whocould sometimes argue withthem. ~~~~~~~~earlier versions ofthesoftware hasbeendonewith XMD and * todisplay colored images;

  • SACI - Developing a Logopaedic Mobile Device Using an FPGA
    2007 4th International Symposium on Applied Computational Intelligence and Informatics, 2007
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe Pentiuc, Tudor Ioan Cerlinca
    Abstract:

    In this paper we will describe a device developed for children with speech Disorders caused by bad pronunciation or even by bad hearing of certain phonemes. First, we will define speech Fluency and speech Fluency Disorder. Speech Fluency designates the easy and confidant construction of a phonic stream that is understandable to the receiver. Speech Fluency Disorder points to the elements which significantly impair the construction of the phonic stream and which make understanding by the receiver difficult (a partial definition, illocutionary connector in the form of a metalingual commentary). Speech Fluency Disorder, so defined, is an inherent quality of stuttering [1].

  • Developing a Logopaedic Mobile Device Using an FPGA
    2007 4th International Symposium on Applied Computational Intelligence and Informatics, 2007
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe Pentiuc, Tudor Ioan Cerlinca
    Abstract:

    In this paper we will describe a device developed for children with speech Disorders caused by bad pronunciation or even by bad hearing of certain phonemes. First, we will define speech Fluency and speech Fluency Disorder. Speech Fluency designates the easy and confidant construction of a phonic stream that is understandable to the receiver. Speech Fluency Disorder points to the elements which significantly impair the construction of the phonic stream and which make understanding by the receiver difficult (a partial definition, illocutionary connector in the form of a metalingual commentary). Speech Fluency Disorder, so defined, is an inherent quality of stuttering [1].

Tudor Ioan Cerlinca - One of the best experts on this subject based on the ideXlab platform.

  • SACI - Developing a Logopaedic Mobile Device Using an FPGA
    2007 4th International Symposium on Applied Computational Intelligence and Informatics, 2007
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe Pentiuc, Tudor Ioan Cerlinca
    Abstract:

    In this paper we will describe a device developed for children with speech Disorders caused by bad pronunciation or even by bad hearing of certain phonemes. First, we will define speech Fluency and speech Fluency Disorder. Speech Fluency designates the easy and confidant construction of a phonic stream that is understandable to the receiver. Speech Fluency Disorder points to the elements which significantly impair the construction of the phonic stream and which make understanding by the receiver difficult (a partial definition, illocutionary connector in the form of a metalingual commentary). Speech Fluency Disorder, so defined, is an inherent quality of stuttering [1].

  • Developing a Logopaedic Mobile Device Using an FPGA
    2007 4th International Symposium on Applied Computational Intelligence and Informatics, 2007
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe Pentiuc, Tudor Ioan Cerlinca
    Abstract:

    In this paper we will describe a device developed for children with speech Disorders caused by bad pronunciation or even by bad hearing of certain phonemes. First, we will define speech Fluency and speech Fluency Disorder. Speech Fluency designates the easy and confidant construction of a phonic stream that is understandable to the receiver. Speech Fluency Disorder points to the elements which significantly impair the construction of the phonic stream and which make understanding by the receiver difficult (a partial definition, illocutionary connector in the form of a metalingual commentary). Speech Fluency Disorder, so defined, is an inherent quality of stuttering [1].

Ju Shen - One of the best experts on this subject based on the ideXlab platform.

  • Automatic video self modeling for voice Disorder
    Multimedia Tools and Applications, 2015
    Co-Authors: Ju Shen, Changpeng Ti, Anand Raghunathan, Sen-ching S. Cheung, Rita Patel
    Abstract:

    Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with Fluency Disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice Disorders. Starting with a video recording of a voice-Disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient’s original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Results of both objective measurements and subjective evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques.

  • Automatic video self modeling for voice Disorder
    Multimedia Tools and Applications, 2015
    Co-Authors: Ju Shen, Changpeng Ti, Anand Raghunathan, Sen-ching S. Cheung, Rita Patel
    Abstract:

    Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with Fluency Disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice Disorders. Starting with a video recording of a voice-Disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient's original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Results of both objective measurements and subjective evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques. © 2014 Springer Science+Business Media New York.

  • Automatic lip-synchronized video-self-modeling intervention for voice Disorders
    2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), 2012
    Co-Authors: Ju Shen, Changpeng Ti, Sen-ching S. Cheung, Rita R. Patel
    Abstract:

    Video self-modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with Fluency Disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice Disorders. Starting with a video recording of a voice-Disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient's original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Experimental evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques.

Rita Patel - One of the best experts on this subject based on the ideXlab platform.

  • Automatic video self modeling for voice Disorder
    Multimedia Tools and Applications, 2015
    Co-Authors: Ju Shen, Changpeng Ti, Anand Raghunathan, Sen-ching S. Cheung, Rita Patel
    Abstract:

    Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with Fluency Disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice Disorders. Starting with a video recording of a voice-Disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient’s original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Results of both objective measurements and subjective evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques.

  • Automatic video self modeling for voice Disorder
    Multimedia Tools and Applications, 2015
    Co-Authors: Ju Shen, Changpeng Ti, Anand Raghunathan, Sen-ching S. Cheung, Rita Patel
    Abstract:

    Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with Fluency Disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice Disorders. Starting with a video recording of a voice-Disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient's original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Results of both objective measurements and subjective evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques. © 2014 Springer Science+Business Media New York.

Adrian Graur - One of the best experts on this subject based on the ideXlab platform.

  • Developing aLogopaedic Mobile Device UsinganFPGA
    2020
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe
    Abstract:

    Inthis paper wewill describe adevice developed for * thedimensions ofthefinal device should besmall children withspeech Disorders caused bybadpronunciation or enough tobeusedevenbysmall children; evenbybadhearing ofcertain phonemes. First, wewilldefine * tohavearechargeable battery. speechFluency andspeechFluency Disorder. SpeechFluency designates theeasyandconfidant construction ofaphonic stream thatisunderstandable tothereceiver. Speech Fluency Disorder III. WHY FPGA? points totheelements whichsignificantly impair theconstruction Inorder toanswer thisquestion, wehavetodetermine the ofthephonic stream andwhichmakeunderstanding bythe hardware requirements forourmobiledevice: receiver difficult (apartial definition, illocutionary connector in * amicroprocessor (faster isbetter); theformofametalingual commentary). Speech Fluency Disorder, * amicrophone andaspeaker; sodefined, isaninherent quality ofstuttering 111. * aADC forrecording audiosamples andaDAC for ~~~~~~~~~~~there weren't PDAswithADC/DACconverters and, second, small children dowiththis device should besimilar with t w simple gamesthat sometimes give bonuses totheplayer. we needsome realtime responses fromthedevice, whichis Themainidea behind theconcept ofamobile device for notthecasewithPDAoperating system. children withspeech Disorder isthat, sometimes, they can Eventually, we hadtochoose fromtheother twopossible interact better with alifeless device thanwith ahumanbeingsolutions. We choose theFPGAbecause we candesign/use a whocould sometimes argue withthem. ~~~~~~~~earlier versions ofthesoftware hasbeendonewith XMD and * todisplay colored images;

  • SACI - Developing a Logopaedic Mobile Device Using an FPGA
    2007 4th International Symposium on Applied Computational Intelligence and Informatics, 2007
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe Pentiuc, Tudor Ioan Cerlinca
    Abstract:

    In this paper we will describe a device developed for children with speech Disorders caused by bad pronunciation or even by bad hearing of certain phonemes. First, we will define speech Fluency and speech Fluency Disorder. Speech Fluency designates the easy and confidant construction of a phonic stream that is understandable to the receiver. Speech Fluency Disorder points to the elements which significantly impair the construction of the phonic stream and which make understanding by the receiver difficult (a partial definition, illocutionary connector in the form of a metalingual commentary). Speech Fluency Disorder, so defined, is an inherent quality of stuttering [1].

  • Developing a Logopaedic Mobile Device Using an FPGA
    2007 4th International Symposium on Applied Computational Intelligence and Informatics, 2007
    Co-Authors: Marius Cristian Cerlinca, Adrian Graur, Stefan Gheorghe Pentiuc, Tudor Ioan Cerlinca
    Abstract:

    In this paper we will describe a device developed for children with speech Disorders caused by bad pronunciation or even by bad hearing of certain phonemes. First, we will define speech Fluency and speech Fluency Disorder. Speech Fluency designates the easy and confidant construction of a phonic stream that is understandable to the receiver. Speech Fluency Disorder points to the elements which significantly impair the construction of the phonic stream and which make understanding by the receiver difficult (a partial definition, illocutionary connector in the form of a metalingual commentary). Speech Fluency Disorder, so defined, is an inherent quality of stuttering [1].