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Human Language Technology

Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking


The purpose of this study is to detect the mismatch between text script and voice-over. For this, we present a novel utterance verification (UV) method, which calculates the degree of correspondence between a voice-over and the phoneme sequence of a script. We found that the phoneme recognition probabilities of exaggerated voice-overs decrease compared to ordinary utterances, but their rankings do not demonstrate any significant change.

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Authors:
Yoonjae Jeong, Hoon-Young Cho
Submitted On:
21 May 2020 - 7:57am
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ICASSP2020_YJEONG_SLIDES.pdf

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[1] Yoonjae Jeong, Hoon-Young Cho, "Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5426. Accessed: Jul. 07, 2020.
@article{5426-20,
url = {http://sigport.org/5426},
author = {Yoonjae Jeong; Hoon-Young Cho },
publisher = {IEEE SigPort},
title = {Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking},
year = {2020} }
TY - EJOUR
T1 - Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking
AU - Yoonjae Jeong; Hoon-Young Cho
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5426
ER -
Yoonjae Jeong, Hoon-Young Cho. (2020). Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking. IEEE SigPort. http://sigport.org/5426
Yoonjae Jeong, Hoon-Young Cho, 2020. Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking. Available at: http://sigport.org/5426.
Yoonjae Jeong, Hoon-Young Cho. (2020). "Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking." Web.
1. Yoonjae Jeong, Hoon-Young Cho. Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5426

Learning Motion Disfluencies for Automatic Sign Language Segmentation


We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features.

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Authors:
Iva Farag
Submitted On:
9 May 2019 - 2:18am
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[1] Iva Farag, "Learning Motion Disfluencies for Automatic Sign Language Segmentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4154. Accessed: Jul. 07, 2020.
@article{4154-19,
url = {http://sigport.org/4154},
author = {Iva Farag },
publisher = {IEEE SigPort},
title = {Learning Motion Disfluencies for Automatic Sign Language Segmentation},
year = {2019} }
TY - EJOUR
T1 - Learning Motion Disfluencies for Automatic Sign Language Segmentation
AU - Iva Farag
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4154
ER -
Iva Farag. (2019). Learning Motion Disfluencies for Automatic Sign Language Segmentation. IEEE SigPort. http://sigport.org/4154
Iva Farag, 2019. Learning Motion Disfluencies for Automatic Sign Language Segmentation. Available at: http://sigport.org/4154.
Iva Farag. (2019). "Learning Motion Disfluencies for Automatic Sign Language Segmentation." Web.
1. Iva Farag. Learning Motion Disfluencies for Automatic Sign Language Segmentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4154

Whole Sentence Neural Language Model


Recurrent neural networks have become increasingly popular for the task of language modeling achieving impressive gains in state-of-the-art speech recognition and natural language processing (NLP) tasks. Recurrent models exploit word dependencies over a much longer context window (as retained by the history states) than what is feasible with n-gram language models.

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Authors:
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran
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20 April 2018 - 10:30pm
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whole sentence neural language model

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[1] Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran, "Whole Sentence Neural Language Model ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3118. Accessed: Jul. 07, 2020.
@article{3118-18,
url = {http://sigport.org/3118},
author = {Abhinav Sethy; Kartik Audhkhasi; Bhuvana Ramabhadran },
publisher = {IEEE SigPort},
title = {Whole Sentence Neural Language Model },
year = {2018} }
TY - EJOUR
T1 - Whole Sentence Neural Language Model
AU - Abhinav Sethy; Kartik Audhkhasi; Bhuvana Ramabhadran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3118
ER -
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran. (2018). Whole Sentence Neural Language Model . IEEE SigPort. http://sigport.org/3118
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran, 2018. Whole Sentence Neural Language Model . Available at: http://sigport.org/3118.
Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran. (2018). "Whole Sentence Neural Language Model ." Web.
1. Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran. Whole Sentence Neural Language Model [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3118

END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING

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Authors:
Lei Chen, Jidong Tao, Shabnam Ghaffarzadegan, Yao Qian
Submitted On:
19 April 2018 - 2:45pm
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icassp2018_final.pdf

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[1] Lei Chen, Jidong Tao, Shabnam Ghaffarzadegan, Yao Qian, "END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2999. Accessed: Jul. 07, 2020.
@article{2999-18,
url = {http://sigport.org/2999},
author = {Lei Chen; Jidong Tao; Shabnam Ghaffarzadegan; Yao Qian },
publisher = {IEEE SigPort},
title = {END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING},
year = {2018} }
TY - EJOUR
T1 - END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING
AU - Lei Chen; Jidong Tao; Shabnam Ghaffarzadegan; Yao Qian
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2999
ER -
Lei Chen, Jidong Tao, Shabnam Ghaffarzadegan, Yao Qian. (2018). END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING. IEEE SigPort. http://sigport.org/2999
Lei Chen, Jidong Tao, Shabnam Ghaffarzadegan, Yao Qian, 2018. END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING. Available at: http://sigport.org/2999.
Lei Chen, Jidong Tao, Shabnam Ghaffarzadegan, Yao Qian. (2018). "END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING." Web.
1. Lei Chen, Jidong Tao, Shabnam Ghaffarzadegan, Yao Qian. END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2999