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Human Spoken Language Acquisition, Development and Learning (SLP-LADL)

DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES


This paper describes new reference benchmark results based on hybrid Hidden Markov Model and Deep Neural Networks (HMM-DNN) for the GlobalPhone (GP) multilingual text and speech database. GP is a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation dictionaries in more than 20 languages. Moreover, we provide new results for five additional languages, namely, Amharic, Oromo, Tigrigna, Wolaytta, and Uyghur.

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Authors:
Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz
Submitted On:
20 May 2020 - 9:12am
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[1] Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz, "DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5407. Accessed: Jul. 09, 2020.
@article{5407-20,
url = {http://sigport.org/5407},
author = {Martha Yifiru Tachbelie; Ayimunishagu Abulimiti; Solomon Teferra Abate; Tanja Schultz },
publisher = {IEEE SigPort},
title = {DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES},
year = {2020} }
TY - EJOUR
T1 - DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES
AU - Martha Yifiru Tachbelie; Ayimunishagu Abulimiti; Solomon Teferra Abate; Tanja Schultz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5407
ER -
Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz. (2020). DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES. IEEE SigPort. http://sigport.org/5407
Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz, 2020. DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES. Available at: http://sigport.org/5407.
Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz. (2020). "DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES." Web.
1. Martha Yifiru Tachbelie, Ayimunishagu Abulimiti, Solomon Teferra Abate, Tanja Schultz. DNN-BASED SPEECH RECOGNITION FOR GLOBALPHONE LANGUAGES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5407

DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES


In this work, we present speech recognition systems for four Ethiopian languages: Amharic, Tigrigna, Oromo and Wolaytta. We have used comparable training corpora of about 20 to 29 hours speech and evaluation speech of about 1 hour for each of the languages. For Amharic and Tigrigna, lexical and language models of different vocabulary size have been developed. For Oromo and Wolaytta, the training lexicons have been used for decoding.

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Authors:
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz
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20 May 2020 - 9:24am
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[1] Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz, "DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5395. Accessed: Jul. 09, 2020.
@article{5395-20,
url = {http://sigport.org/5395},
author = {Solomon Teferra Abate;Martha Yifiru Tachbelie and Tanja Schultz },
publisher = {IEEE SigPort},
title = {DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES},
year = {2020} }
TY - EJOUR
T1 - DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES
AU - Solomon Teferra Abate;Martha Yifiru Tachbelie and Tanja Schultz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5395
ER -
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz. (2020). DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES. IEEE SigPort. http://sigport.org/5395
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz, 2020. DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES. Available at: http://sigport.org/5395.
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz. (2020). "DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES." Web.
1. Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz. DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5395

SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION


The process of spoken language acquisition has been one of the topics which attract the greatest interesting from linguists for decades. By utilizing modern machine learning techniques, we simulated this process on computers, which helps to understand the mystery behind the process, and enable new possibilities of applying this concept on, but not limited to, intelligent robots. This paper proposes a new framework for simulating spoken language acquisition by combining reinforcement learning and unsupervised learning methods.

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Authors:
Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki
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18 May 2020 - 3:34am
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[1] Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki, "SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5390. Accessed: Jul. 09, 2020.
@article{5390-20,
url = {http://sigport.org/5390},
author = {Shengzhou Gao; Wenxin Hou; Tomohiro Tanaka; Takahiro Shinozaki },
publisher = {IEEE SigPort},
title = {SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION},
year = {2020} }
TY - EJOUR
T1 - SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION
AU - Shengzhou Gao; Wenxin Hou; Tomohiro Tanaka; Takahiro Shinozaki
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5390
ER -
Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki. (2020). SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION. IEEE SigPort. http://sigport.org/5390
Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki, 2020. SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION. Available at: http://sigport.org/5390.
Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki. (2020). "SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION." Web.
1. Shengzhou Gao, Wenxin Hou, Tomohiro Tanaka, Takahiro Shinozaki. SPOKEN LANGUAGE ACQUISITION BASED ON REINFORCEMENT LEARNING AND WORD UNIT SEGMENTATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5390

Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings


Involvement hot spots have been proposed as a useful concept for meeting analysis and studied off and on for over 15 years. These are regions of meetings that are marked by high participant involvement, as judged by human annotators. However, prior work was either not conducted in a formal machine learning setting, or focused on only a subset of possible meeting features or downstream applications (such as summarization). In this paper we investigate to what extent various acoustic, linguistic and pragmatic aspects of the meetings, both in isolation and jointly, can help detect hot spots.

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Authors:
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke
Submitted On:
15 May 2020 - 1:11am
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[1] Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke, "Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5334. Accessed: Jul. 09, 2020.
@article{5334-20,
url = {http://sigport.org/5334},
author = {Dave Makhervaks; William Hinthorn; Dimitrios Dimitriadis; Andreas Stolcke },
publisher = {IEEE SigPort},
title = {Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings},
year = {2020} }
TY - EJOUR
T1 - Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings
AU - Dave Makhervaks; William Hinthorn; Dimitrios Dimitriadis; Andreas Stolcke
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5334
ER -
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke. (2020). Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings. IEEE SigPort. http://sigport.org/5334
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke, 2020. Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings. Available at: http://sigport.org/5334.
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke. (2020). "Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings." Web.
1. Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke. Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5334

SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION


Data augmentation is crucial to improving the performance of deep neural networks by helping the model avoid overfitting and improve its generalization. In automatic speech recognition, previous work proposed several approaches to augment data by performing speed perturbation or spectral transformation. Since data augmented in these manners has similar acoustic representations with the original data, it has limited advantage in improving generalization of the acoustic model.

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Authors:
Sangki Kim, Yeha Lee
Submitted On:
10 May 2019 - 9:55am
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[1] Sangki Kim, Yeha Lee, "SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4337. Accessed: Jul. 09, 2020.
@article{4337-19,
url = {http://sigport.org/4337},
author = { Sangki Kim; Yeha Lee },
publisher = {IEEE SigPort},
title = {SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION
AU - Sangki Kim; Yeha Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4337
ER -
Sangki Kim, Yeha Lee. (2019). SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/4337
Sangki Kim, Yeha Lee, 2019. SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION. Available at: http://sigport.org/4337.
Sangki Kim, Yeha Lee. (2019). "SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION." Web.
1. Sangki Kim, Yeha Lee. SPEECH AUGMENTATION USING WAVENET IN SPEECH RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4337

Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese


We investigate the behaviour of attention in neural models of visually grounded speech trained on two languages: English and Japanese. Experimental results show that attention focuses on nouns and this behaviour holds true for two very typologically different languages. We also draw parallels between artificial neural attention and human attention and show that neural attention focuses on word endings as it has been theorised for human attention. Finally, we investigate how two visually grounded monolingual models can be used to perform cross-lingual speech-to-speech retrieval.

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Authors:
Jean-Pierre Chevrot, Laurent Besacier
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8 May 2019 - 6:18am
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[1] Jean-Pierre Chevrot, Laurent Besacier, "Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4066. Accessed: Jul. 09, 2020.
@article{4066-19,
url = {http://sigport.org/4066},
author = {Jean-Pierre Chevrot; Laurent Besacier },
publisher = {IEEE SigPort},
title = {Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese},
year = {2019} }
TY - EJOUR
T1 - Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese
AU - Jean-Pierre Chevrot; Laurent Besacier
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4066
ER -
Jean-Pierre Chevrot, Laurent Besacier. (2019). Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese. IEEE SigPort. http://sigport.org/4066
Jean-Pierre Chevrot, Laurent Besacier, 2019. Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese. Available at: http://sigport.org/4066.
Jean-Pierre Chevrot, Laurent Besacier. (2019). "Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese." Web.
1. Jean-Pierre Chevrot, Laurent Besacier. Models of visually grounded speech signal pay attention to nouns: a bilingual experiment on English and Japanese [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4066

Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies


In the present study, the ultrasonic data of two prelingual deaf participants were collected to observe tongue movements during the production of all the apical syllables under four citation tones except for \emph{ri} in Mandarin Chinese. Results of data analysis showed that, besides their personal characteristics, the two participants share similar problems in producing those apical syllables such as producing alveolar syllables as post-alveolar syllables, realizing affricates as fricatives, and unable to pronounce some types of apical syllables which they can perceive correctly.

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Authors:
Quan Zhou, Yu Chen, Yanting Chen, Hao Zhang, Jianguo Wei, Jianwu Dang
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16 October 2016 - 11:58pm
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[1] Quan Zhou, Yu Chen, Yanting Chen, Hao Zhang, Jianguo Wei, Jianwu Dang, "Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1258. Accessed: Jul. 09, 2020.
@article{1258-16,
url = {http://sigport.org/1258},
author = {Quan Zhou; Yu Chen; Yanting Chen; Hao Zhang; Jianguo Wei; Jianwu Dang },
publisher = {IEEE SigPort},
title = {Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies},
year = {2016} }
TY - EJOUR
T1 - Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies
AU - Quan Zhou; Yu Chen; Yanting Chen; Hao Zhang; Jianguo Wei; Jianwu Dang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1258
ER -
Quan Zhou, Yu Chen, Yanting Chen, Hao Zhang, Jianguo Wei, Jianwu Dang. (2016). Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies. IEEE SigPort. http://sigport.org/1258
Quan Zhou, Yu Chen, Yanting Chen, Hao Zhang, Jianguo Wei, Jianwu Dang, 2016. Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies. Available at: http://sigport.org/1258.
Quan Zhou, Yu Chen, Yanting Chen, Hao Zhang, Jianguo Wei, Jianwu Dang. (2016). "Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies." Web.
1. Quan Zhou, Yu Chen, Yanting Chen, Hao Zhang, Jianguo Wei, Jianwu Dang. Tongue Performance in Articulating Mandarin Apical Syllables by Prelingual Deaf Adults Using Ultrasonic Technology: Two Case Studies [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1258

Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment


This study investigates how automatic scorings based on speech technology can affect human raters' judgement of students' oral language proficiency in L2 speaking tests. Automatic scorings based on ASR are widely used in non-critical speaking tests or practices and relatively high correlations between machine scores and human scores have been reported. In high-stakes speaking tests, however, many teachers remain skeptical about the fairness of automatic scores given by machines even with the most advanced scoring methods.

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Authors:
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang
Submitted On:
16 October 2016 - 11:17pm
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[1] Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang, "Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1210. Accessed: Jul. 09, 2020.
@article{1210-16,
url = {http://sigport.org/1210},
author = {Dean Luo; Wentao Gu; Ruxin Luo; Lixin Wang },
publisher = {IEEE SigPort},
title = {Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment},
year = {2016} }
TY - EJOUR
T1 - Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment
AU - Dean Luo; Wentao Gu; Ruxin Luo; Lixin Wang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1210
ER -
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang. (2016). Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment. IEEE SigPort. http://sigport.org/1210
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang, 2016. Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment. Available at: http://sigport.org/1210.
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang. (2016). "Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment." Web.
1. Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang. Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1210

Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment


This study investigates how automatic scorings based on speech technology can affect human raters' judgement of students' oral language proficiency in L2 speaking tests. Automatic scorings based on ASR are widely used in non-critical speaking tests or practices and relatively high correlations between machine scores and human scores have been reported. In high-stakes speaking tests, however, many teachers remain skeptical about the fairness of automatic scores given by machines even with the most advanced scoring methods.

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Authors:
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang
Submitted On:
14 October 2016 - 12:37pm
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[1] Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang, "Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1209. Accessed: Jul. 09, 2020.
@article{1209-16,
url = {http://sigport.org/1209},
author = {Dean Luo; Wentao Gu; Ruxin Luo; Lixin Wang },
publisher = {IEEE SigPort},
title = {Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment},
year = {2016} }
TY - EJOUR
T1 - Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment
AU - Dean Luo; Wentao Gu; Ruxin Luo; Lixin Wang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1209
ER -
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang. (2016). Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment. IEEE SigPort. http://sigport.org/1209
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang, 2016. Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment. Available at: http://sigport.org/1209.
Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang. (2016). "Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment." Web.
1. Dean Luo, Wentao Gu, Ruxin Luo, Lixin Wang. Investigation of the Effects of Automatic Scoring Technology on Human Raters' Performances in L2 Speech Proficiency Assessment [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1209

Rich Punctuations Prediction Using Large-scale Deep Learning


Punctuation plays an important role in language processing. However, automatic speech recognition systems only output plain word sequences. It is then of interest to predict punctuations on plain word sequences. Previous works have focused on using lexical features or prosodic cues captured from small corpus to predict simple punctuations. Compared with simple punctuations, rich punctuations provide more meaningful

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Authors:
Xueyang Wu, Su Zhu, Yue Wu, and Kai Yu
Submitted On:
14 October 2016 - 2:50am
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[1] Xueyang Wu, Su Zhu, Yue Wu, and Kai Yu, "Rich Punctuations Prediction Using Large-scale Deep Learning", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1181. Accessed: Jul. 09, 2020.
@article{1181-16,
url = {http://sigport.org/1181},
author = {Xueyang Wu; Su Zhu; Yue Wu; and Kai Yu },
publisher = {IEEE SigPort},
title = {Rich Punctuations Prediction Using Large-scale Deep Learning},
year = {2016} }
TY - EJOUR
T1 - Rich Punctuations Prediction Using Large-scale Deep Learning
AU - Xueyang Wu; Su Zhu; Yue Wu; and Kai Yu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1181
ER -
Xueyang Wu, Su Zhu, Yue Wu, and Kai Yu. (2016). Rich Punctuations Prediction Using Large-scale Deep Learning. IEEE SigPort. http://sigport.org/1181
Xueyang Wu, Su Zhu, Yue Wu, and Kai Yu, 2016. Rich Punctuations Prediction Using Large-scale Deep Learning. Available at: http://sigport.org/1181.
Xueyang Wu, Su Zhu, Yue Wu, and Kai Yu. (2016). "Rich Punctuations Prediction Using Large-scale Deep Learning." Web.
1. Xueyang Wu, Su Zhu, Yue Wu, and Kai Yu. Rich Punctuations Prediction Using Large-scale Deep Learning [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1181

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