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Acoustic Modeling for Automatic Speech Recognition (SPE-RECO)

Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning


Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. One approach is the attention-based encoder decoder framework that learns a mapping between variable-length input and output sequences in one step using a purely data-driven method.

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Authors:
Suyoun Kim, Takaaki Hori, Shinji Watanabe
Submitted On:
7 March 2017 - 4:58pm
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joint ctc attention

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[1] Suyoun Kim, Takaaki Hori, Shinji Watanabe, "Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1695. Accessed: Aug. 22, 2017.
@article{1695-17,
url = {http://sigport.org/1695},
author = {Suyoun Kim; Takaaki Hori; Shinji Watanabe },
publisher = {IEEE SigPort},
title = {Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning},
year = {2017} }
TY - EJOUR
T1 - Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning
AU - Suyoun Kim; Takaaki Hori; Shinji Watanabe
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1695
ER -
Suyoun Kim, Takaaki Hori, Shinji Watanabe. (2017). Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning. IEEE SigPort. http://sigport.org/1695
Suyoun Kim, Takaaki Hori, Shinji Watanabe, 2017. Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning. Available at: http://sigport.org/1695.
Suyoun Kim, Takaaki Hori, Shinji Watanabe. (2017). "Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning." Web.
1. Suyoun Kim, Takaaki Hori, Shinji Watanabe. Joint CTC-Attention based End-to-End Speech Recognition using Multi-Task Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1695

LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS


Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond to context-dependent tied states or senones. The present work addresses some limitations of GMM-HMM senone alignments for DNN training. We hypothesize that the senone probabilities obtained from a DNN trained with binary labels can provide more accurate targets to learn better acoustic models.

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Authors:
Pranay Dighe, Afsaneh Asaei, Hervé Bourlard
Submitted On:
7 March 2017 - 12:15pm
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icassp_poster.pdf

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[1] Pranay Dighe, Afsaneh Asaei, Hervé Bourlard, "LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1687. Accessed: Aug. 22, 2017.
@article{1687-17,
url = {http://sigport.org/1687},
author = {Pranay Dighe; Afsaneh Asaei; Hervé Bourlard },
publisher = {IEEE SigPort},
title = {LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS},
year = {2017} }
TY - EJOUR
T1 - LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS
AU - Pranay Dighe; Afsaneh Asaei; Hervé Bourlard
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1687
ER -
Pranay Dighe, Afsaneh Asaei, Hervé Bourlard. (2017). LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS. IEEE SigPort. http://sigport.org/1687
Pranay Dighe, Afsaneh Asaei, Hervé Bourlard, 2017. LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS. Available at: http://sigport.org/1687.
Pranay Dighe, Afsaneh Asaei, Hervé Bourlard. (2017). "LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS." Web.
1. Pranay Dighe, Afsaneh Asaei, Hervé Bourlard. LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER ! DNN ACOUSTIC MODELS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1687

Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures

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6 March 2017 - 3:53pm
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evaluating-automatic-speech.pdf

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[1] , "Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1663. Accessed: Aug. 22, 2017.
@article{1663-17,
url = {http://sigport.org/1663},
author = { },
publisher = {IEEE SigPort},
title = {Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures},
year = {2017} }
TY - EJOUR
T1 - Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1663
ER -
. (2017). Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures. IEEE SigPort. http://sigport.org/1663
, 2017. Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures. Available at: http://sigport.org/1663.
. (2017). "Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures." Web.
1. . Evaluating Automatic Speech Recognition Systems in Compar ison with Human Perception Results Using Distinctive Feature Measures [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1663

ICASSP2017 Poster (Paper #4319)


The performance of automatic speech recognition (ASR) system is often degraded in adverse real-world environments. In recent times, deep learning has successfully emerged as a breakthrough for acoustic modeling in ASR; accordingly, deep-neural-network(DNN)-based speech feature enhancement (FE) approaches have attracted much attention owing to their powerful modeling capabilities. However, DNN-based approaches are unable to achieve remarkable performance improvements for speech with severe distortion in the test environments different from training environments.

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Authors:
Ho-Yong Lee, Ji-Won Cho, Minook Kim, and Hyung-Min Park
Submitted On:
3 March 2017 - 8:36pm
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poster_icassp2017_hpark.pdf

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[1] Ho-Yong Lee, Ji-Won Cho, Minook Kim, and Hyung-Min Park, "ICASSP2017 Poster (Paper #4319)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1620. Accessed: Aug. 22, 2017.
@article{1620-17,
url = {http://sigport.org/1620},
author = {Ho-Yong Lee; Ji-Won Cho; Minook Kim; and Hyung-Min Park },
publisher = {IEEE SigPort},
title = {ICASSP2017 Poster (Paper #4319)},
year = {2017} }
TY - EJOUR
T1 - ICASSP2017 Poster (Paper #4319)
AU - Ho-Yong Lee; Ji-Won Cho; Minook Kim; and Hyung-Min Park
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1620
ER -
Ho-Yong Lee, Ji-Won Cho, Minook Kim, and Hyung-Min Park. (2017). ICASSP2017 Poster (Paper #4319). IEEE SigPort. http://sigport.org/1620
Ho-Yong Lee, Ji-Won Cho, Minook Kim, and Hyung-Min Park, 2017. ICASSP2017 Poster (Paper #4319). Available at: http://sigport.org/1620.
Ho-Yong Lee, Ji-Won Cho, Minook Kim, and Hyung-Min Park. (2017). "ICASSP2017 Poster (Paper #4319)." Web.
1. Ho-Yong Lee, Ji-Won Cho, Minook Kim, and Hyung-Min Park. ICASSP2017 Poster (Paper #4319) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1620

Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training

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Authors:
Chao Zhang, Phil Woodland
Submitted On:
8 March 2017 - 7:55am
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ICASSP-cz277-v6.pdf

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[1] Chao Zhang, Phil Woodland, "Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1618. Accessed: Aug. 22, 2017.
@article{1618-17,
url = {http://sigport.org/1618},
author = {Chao Zhang; Phil Woodland },
publisher = {IEEE SigPort},
title = {Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training},
year = {2017} }
TY - EJOUR
T1 - Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training
AU - Chao Zhang; Phil Woodland
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1618
ER -
Chao Zhang, Phil Woodland. (2017). Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training. IEEE SigPort. http://sigport.org/1618
Chao Zhang, Phil Woodland, 2017. Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training. Available at: http://sigport.org/1618.
Chao Zhang, Phil Woodland. (2017). "Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training." Web.
1. Chao Zhang, Phil Woodland. Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1618

PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA


It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way.

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Authors:
Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee, Lin-Shan Lee
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1 March 2017 - 12:56am
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PTDNN_ICASSP2017_Poster_v5.3.pdf

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[1] Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee, Lin-Shan Lee, "PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1531. Accessed: Aug. 22, 2017.
@article{1531-17,
url = {http://sigport.org/1531},
author = {Cheng-Kuan Wei; Cheng-Tao Chung; Hung-Yi Lee; Lin-Shan Lee },
publisher = {IEEE SigPort},
title = {PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA},
year = {2017} }
TY - EJOUR
T1 - PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA
AU - Cheng-Kuan Wei; Cheng-Tao Chung; Hung-Yi Lee; Lin-Shan Lee
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1531
ER -
Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee, Lin-Shan Lee. (2017). PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA. IEEE SigPort. http://sigport.org/1531
Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee, Lin-Shan Lee, 2017. PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA. Available at: http://sigport.org/1531.
Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee, Lin-Shan Lee. (2017). "PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA." Web.
1. Cheng-Kuan Wei, Cheng-Tao Chung, Hung-Yi Lee, Lin-Shan Lee. PERSONALIZED ACOUSTIC MODELING BY WEAKLY SUPERVISED MULTI-TASK DEEP LEARNING USING ACOUSTIC TOKENS DISCOVERED FROM UNLABELED DATA [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1531

MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION


Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties behind the remarkable performance remain unclear in many applications, e.g., automatic speech recognition (ASR). This paper employs visualization techniques to study the behavior of LSTM and GRU when performing speech recognition tasks.

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Authors:
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang
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4 March 2017 - 3:11am
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icassp17_visual.pdf

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[1] Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang, "MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1463. Accessed: Aug. 22, 2017.
@article{1463-17,
url = {http://sigport.org/1463},
author = {Zhiyuan Tang; Ying Shi; Dong Wang; Yang Feng; Shiyue Zhang },
publisher = {IEEE SigPort},
title = {MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION
AU - Zhiyuan Tang; Ying Shi; Dong Wang; Yang Feng; Shiyue Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1463
ER -
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang. (2017). MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/1463
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang, 2017. MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION. Available at: http://sigport.org/1463.
Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang. (2017). "MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION." Web.
1. Zhiyuan Tang, Ying Shi, Dong Wang, Yang Feng, Shiyue Zhang. MEMORY VISUALIZATION FOR GATED RECURRENT NEURAL NETWORKS IN SPEECH RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1463

Exploring Tonal Information for Lhasa Dialect Acoustic Modeling


Detailed analysis of tonal features for Tibetan Lhasa dialect is an important task for Tibetan automatic speech recognition (ASR) applications. However, it is difficult to utilize tonal information because it remains controversial how many tonal patterns the Lhasa dialect has. Therefore, few studies have focused on modeling the tonal information of the Lhasa dialect for speech recognition purpose. For this reason, we investigated influences of the tonal information on the performance of Lhasa Tibetan speech recognition.

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Authors:
Jian Li, Hongcui Wang, Longbiao Wang, Jianwu Dang, Kuntharrgyal khuru, Gyaltsen Lobsang
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14 October 2016 - 11:52am
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Exploring Tonal Information for Lhasa Dialect Acoustic Modeling

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[1] Jian Li, Hongcui Wang, Longbiao Wang, Jianwu Dang, Kuntharrgyal khuru, Gyaltsen Lobsang, "Exploring Tonal Information for Lhasa Dialect Acoustic Modeling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1205. Accessed: Aug. 22, 2017.
@article{1205-16,
url = {http://sigport.org/1205},
author = {Jian Li; Hongcui Wang; Longbiao Wang; Jianwu Dang; Kuntharrgyal khuru; Gyaltsen Lobsang },
publisher = {IEEE SigPort},
title = {Exploring Tonal Information for Lhasa Dialect Acoustic Modeling},
year = {2016} }
TY - EJOUR
T1 - Exploring Tonal Information for Lhasa Dialect Acoustic Modeling
AU - Jian Li; Hongcui Wang; Longbiao Wang; Jianwu Dang; Kuntharrgyal khuru; Gyaltsen Lobsang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1205
ER -
Jian Li, Hongcui Wang, Longbiao Wang, Jianwu Dang, Kuntharrgyal khuru, Gyaltsen Lobsang. (2016). Exploring Tonal Information for Lhasa Dialect Acoustic Modeling. IEEE SigPort. http://sigport.org/1205
Jian Li, Hongcui Wang, Longbiao Wang, Jianwu Dang, Kuntharrgyal khuru, Gyaltsen Lobsang, 2016. Exploring Tonal Information for Lhasa Dialect Acoustic Modeling. Available at: http://sigport.org/1205.
Jian Li, Hongcui Wang, Longbiao Wang, Jianwu Dang, Kuntharrgyal khuru, Gyaltsen Lobsang. (2016). "Exploring Tonal Information for Lhasa Dialect Acoustic Modeling." Web.
1. Jian Li, Hongcui Wang, Longbiao Wang, Jianwu Dang, Kuntharrgyal khuru, Gyaltsen Lobsang. Exploring Tonal Information for Lhasa Dialect Acoustic Modeling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1205

Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling


This paper describes an investigation on acoustic modeling in the absence of transcribed training data. We propose to use language-mismatched phoneme recognizers to assist unsupervised segmentation and segment clustering of a new language. Using a language-mismatched recognizer, an input utterance is divided into many variable-length segments. Each segment is represented by a feature vector that is derived from the phoneme posterior probabilities.

slides.pdf

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Authors:
Siyuan Feng, Tan Lee, Haipeng Wang
Submitted On:
13 October 2016 - 8:27am
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slides.pdf

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[1] Siyuan Feng, Tan Lee, Haipeng Wang, "Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1170. Accessed: Aug. 22, 2017.
@article{1170-16,
url = {http://sigport.org/1170},
author = {Siyuan Feng; Tan Lee; Haipeng Wang },
publisher = {IEEE SigPort},
title = {Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling},
year = {2016} }
TY - EJOUR
T1 - Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling
AU - Siyuan Feng; Tan Lee; Haipeng Wang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1170
ER -
Siyuan Feng, Tan Lee, Haipeng Wang. (2016). Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling. IEEE SigPort. http://sigport.org/1170
Siyuan Feng, Tan Lee, Haipeng Wang, 2016. Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling. Available at: http://sigport.org/1170.
Siyuan Feng, Tan Lee, Haipeng Wang. (2016). "Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling." Web.
1. Siyuan Feng, Tan Lee, Haipeng Wang. Exploiting Language-Mismatched Phoneme Recognizers for Unsupervised Acoustic Modeling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1170

EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION

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7 April 2016 - 4:11am
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txh18-he-icassp16-formal-poster.pptx

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[1] , "EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1087. Accessed: Aug. 22, 2017.
@article{1087-16,
url = {http://sigport.org/1087},
author = { },
publisher = {IEEE SigPort},
title = {EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION},
year = {2016} }
TY - EJOUR
T1 - EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1087
ER -
. (2016). EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/1087
, 2016. EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION. Available at: http://sigport.org/1087.
. (2016). "EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION." Web.
1. . EXPLOITING LSTM STRUCTURE IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1087

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