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General Topics in Speech Recognition (SPE-GASR)

END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR


The speech chain mechanism integrates automatic speech recognition (ASR) and text-to-speech synthesis (TTS) modules into a single cycle during training. In our previous work, we applied a speech chain mechanism as a semi-supervised learning. It provides the ability for ASR and TTS to assist each other when they receive unpaired data and let them infer the missing pair and optimize the model with reconstruction loss.

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Authors:
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Submitted On:
14 May 2019 - 8:26pm
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[1] Andros Tjandra, Sakriani Sakti, Satoshi Nakamura, "END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4519. Accessed: Sep. 20, 2019.
@article{4519-19,
url = {http://sigport.org/4519},
author = {Andros Tjandra; Sakriani Sakti; Satoshi Nakamura },
publisher = {IEEE SigPort},
title = {END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR},
year = {2019} }
TY - EJOUR
T1 - END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR
AU - Andros Tjandra; Sakriani Sakti; Satoshi Nakamura
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4519
ER -
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. (2019). END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR. IEEE SigPort. http://sigport.org/4519
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura, 2019. END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR. Available at: http://sigport.org/4519.
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. (2019). "END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR." Web.
1. Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4519

Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition


In this paper, we experiment with the recently introduced subword regularization technique \cite{kudo2018subword} in the context of end-to-end automatic speech recognition (ASR). We present results from both attention-based and CTC-based ASR systems on two common benchmark datasets, the 80 hour Wall Street Journal corpus and 1,000 hour Librispeech corpus. We also introduce a novel subword beam search decoding algorithm that significantly improves the final performance of the CTC-based systems.

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Authors:
Jennifer Drexler, James Glass
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14 May 2019 - 9:04am
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[1] Jennifer Drexler, James Glass, "Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4509. Accessed: Sep. 20, 2019.
@article{4509-19,
url = {http://sigport.org/4509},
author = {Jennifer Drexler; James Glass },
publisher = {IEEE SigPort},
title = {Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition},
year = {2019} }
TY - EJOUR
T1 - Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition
AU - Jennifer Drexler; James Glass
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4509
ER -
Jennifer Drexler, James Glass. (2019). Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition. IEEE SigPort. http://sigport.org/4509
Jennifer Drexler, James Glass, 2019. Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition. Available at: http://sigport.org/4509.
Jennifer Drexler, James Glass. (2019). "Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition." Web.
1. Jennifer Drexler, James Glass. Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4509

ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION

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Authors:
Karen Livescu, Michael Picheny
Submitted On:
14 May 2019 - 7:08am
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[1] Karen Livescu, Michael Picheny, "ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4506. Accessed: Sep. 20, 2019.
@article{4506-19,
url = {http://sigport.org/4506},
author = {Karen Livescu; Michael Picheny },
publisher = {IEEE SigPort},
title = {ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION
AU - Karen Livescu; Michael Picheny
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4506
ER -
Karen Livescu, Michael Picheny. (2019). ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/4506
Karen Livescu, Michael Picheny, 2019. ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION. Available at: http://sigport.org/4506.
Karen Livescu, Michael Picheny. (2019). "ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION." Web.
1. Karen Livescu, Michael Picheny. ACOUSTICALLY GROUNDED WORD EMBEDDINGS FOR IMPROVED ACOUSTICS-TO-WORD SPEECH RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4506

LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION


In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained language model (LM). Our proposed fusion methods focus on the memory cell state and the hidden state in the seq2seq decoder long short-term memory (LSTM), and the memory cell state is updated by the LM unlike the prior studies. This means the memory retained by the main seq2seq would be adjusted by the external LM.

Paper Details

Authors:
Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesus Villalba, Najim Dehak
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10 May 2019 - 4:50pm
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Poster

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[1] Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesus Villalba, Najim Dehak, "LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4405. Accessed: Sep. 20, 2019.
@article{4405-19,
url = {http://sigport.org/4405},
author = {Jaejin Cho; Shinji Watanabe; Takaaki Hori; Murali Karthick Baskar; Hirofumi Inaguma; Jesus Villalba; Najim Dehak },
publisher = {IEEE SigPort},
title = {LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION
AU - Jaejin Cho; Shinji Watanabe; Takaaki Hori; Murali Karthick Baskar; Hirofumi Inaguma; Jesus Villalba; Najim Dehak
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4405
ER -
Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesus Villalba, Najim Dehak. (2019). LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/4405
Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesus Villalba, Najim Dehak, 2019. LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION. Available at: http://sigport.org/4405.
Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesus Villalba, Najim Dehak. (2019). "LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION." Web.
1. Jaejin Cho, Shinji Watanabe, Takaaki Hori, Murali Karthick Baskar, Hirofumi Inaguma, Jesus Villalba, Najim Dehak. LANGUAGE MODEL INTEGRATION BASED ON MEMORY CONTROL FOR SEQUENCE TO SEQUENCE SPEECH RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4405

Sequence-to-Sequence ASR Optimization via Reinforcement Learning


Despite the success of sequence-to-sequence approaches in automatic speech recognition (ASR) systems, the models still suffer from several problems, mainly due to the mismatch between the training and inference conditions. In the sequence-to-sequence architecture, the model is trained to predict the grapheme of the current time-step given the input of speech signal and the ground-truth grapheme history of the previous time-steps. However, it remains unclear how well the model approximates real-world speech during inference.

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Authors:
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Submitted On:
14 April 2018 - 10:37am
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[1] Andros Tjandra, Sakriani Sakti, Satoshi Nakamura, "Sequence-to-Sequence ASR Optimization via Reinforcement Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2834. Accessed: Sep. 20, 2019.
@article{2834-18,
url = {http://sigport.org/2834},
author = {Andros Tjandra; Sakriani Sakti; Satoshi Nakamura },
publisher = {IEEE SigPort},
title = {Sequence-to-Sequence ASR Optimization via Reinforcement Learning},
year = {2018} }
TY - EJOUR
T1 - Sequence-to-Sequence ASR Optimization via Reinforcement Learning
AU - Andros Tjandra; Sakriani Sakti; Satoshi Nakamura
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2834
ER -
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. (2018). Sequence-to-Sequence ASR Optimization via Reinforcement Learning. IEEE SigPort. http://sigport.org/2834
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura, 2018. Sequence-to-Sequence ASR Optimization via Reinforcement Learning. Available at: http://sigport.org/2834.
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. (2018). "Sequence-to-Sequence ASR Optimization via Reinforcement Learning." Web.
1. Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. Sequence-to-Sequence ASR Optimization via Reinforcement Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2834

End-to-End Multimodal Speech Recognition


Transcription or sub-titling of open-domain videos is still a chal- lenging domain for Automatic Speech Recognition (ASR) due to the data’s challenging acoustics, variable signal processing and the essentially unrestricted domain of the data. In previous work, we have shown that the visual channel – specifically object and scene features – can help to adapt the acoustic model (AM) and language model (LM) of a recognizer, and we are now expanding this work to end-to-end approaches.

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Authors:
Ramon Sanabria, Florian Metze
Submitted On:
12 April 2018 - 8:02pm
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[1] Ramon Sanabria, Florian Metze, "End-to-End Multimodal Speech Recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2524. Accessed: Sep. 20, 2019.
@article{2524-18,
url = {http://sigport.org/2524},
author = {Ramon Sanabria; Florian Metze },
publisher = {IEEE SigPort},
title = {End-to-End Multimodal Speech Recognition},
year = {2018} }
TY - EJOUR
T1 - End-to-End Multimodal Speech Recognition
AU - Ramon Sanabria; Florian Metze
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2524
ER -
Ramon Sanabria, Florian Metze. (2018). End-to-End Multimodal Speech Recognition. IEEE SigPort. http://sigport.org/2524
Ramon Sanabria, Florian Metze, 2018. End-to-End Multimodal Speech Recognition. Available at: http://sigport.org/2524.
Ramon Sanabria, Florian Metze. (2018). "End-to-End Multimodal Speech Recognition." Web.
1. Ramon Sanabria, Florian Metze. End-to-End Multimodal Speech Recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2524

AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES


There have been several studies, in the recent past, pointing to the
importance of analytic phase of the speech signal in human percep-
tion, especially in noisy conditions. However, phase information is
still not used in state-of-the-art speech recognition systems. In this
paper, we illustrate the importance of analytic phase of the speech
signal for automatic speech recognition. As the computation of ana-
lytic phase suffers from inevitable phase wrapping problem, we ex-
tract features from its time derivative, referred to as instantaneous

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Authors:
Saurabhchand Bhati
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11 November 2017 - 8:10am
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[1] Saurabhchand Bhati, "AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2305. Accessed: Sep. 20, 2019.
@article{2305-17,
url = {http://sigport.org/2305},
author = {Saurabhchand Bhati },
publisher = {IEEE SigPort},
title = {AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES},
year = {2017} }
TY - EJOUR
T1 - AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES
AU - Saurabhchand Bhati
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2305
ER -
Saurabhchand Bhati. (2017). AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES. IEEE SigPort. http://sigport.org/2305
Saurabhchand Bhati, 2017. AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES. Available at: http://sigport.org/2305.
Saurabhchand Bhati. (2017). "AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES." Web.
1. Saurabhchand Bhati. AN INVESTIGATION INTO INSTANTANEOUS FREQUENCY ESTIMATION METHODS FOR IMPROVED SPEECH RECOGNITION FEATURES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2305

Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition

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Authors:
Yan Ji, Hongcui Wang, Bruce Denby
Submitted On:
15 October 2016 - 8:47am
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[1] Yan Ji, Hongcui Wang, Bruce Denby, "Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1235. Accessed: Sep. 20, 2019.
@article{1235-16,
url = {http://sigport.org/1235},
author = {Yan Ji; Hongcui Wang; Bruce Denby },
publisher = {IEEE SigPort},
title = {Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition},
year = {2016} }
TY - EJOUR
T1 - Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition
AU - Yan Ji; Hongcui Wang; Bruce Denby
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1235
ER -
Yan Ji, Hongcui Wang, Bruce Denby. (2016). Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition. IEEE SigPort. http://sigport.org/1235
Yan Ji, Hongcui Wang, Bruce Denby, 2016. Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition. Available at: http://sigport.org/1235.
Yan Ji, Hongcui Wang, Bruce Denby. (2016). "Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition." Web.
1. Yan Ji, Hongcui Wang, Bruce Denby. Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1235

Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition

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Authors:
Yan Ji, Hongcui Wang, Bruce Denby
Submitted On:
15 October 2016 - 8:47am
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[1] Yan Ji, Hongcui Wang, Bruce Denby, "Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1234. Accessed: Sep. 20, 2019.
@article{1234-16,
url = {http://sigport.org/1234},
author = {Yan Ji; Hongcui Wang; Bruce Denby },
publisher = {IEEE SigPort},
title = {Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition},
year = {2016} }
TY - EJOUR
T1 - Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition
AU - Yan Ji; Hongcui Wang; Bruce Denby
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1234
ER -
Yan Ji, Hongcui Wang, Bruce Denby. (2016). Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition. IEEE SigPort. http://sigport.org/1234
Yan Ji, Hongcui Wang, Bruce Denby, 2016. Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition. Available at: http://sigport.org/1234.
Yan Ji, Hongcui Wang, Bruce Denby. (2016). "Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition." Web.
1. Yan Ji, Hongcui Wang, Bruce Denby. Comparison of DCT and Autoencoder-based Features for DNN-HMM Multimodal Silent Speech Recognition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1234

FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION


Examples of subband filters learned using ConvRBM: (a) filters in time-domain (i.e., impulse responses), (b) filters in frequency-domain (i.e., frequency responses).

Convolutional Restricted Boltzmann Machine (ConvRBM) as a model for speech signal is presented in this paper. We have
developed ConvRBM with sampling from noisy rectified linear units (NReLUs). ConvRBM is trained in an unsupervised way to model speech signal of arbitrary lengths. Weights of the model can represent an auditory-like filterbank. Our

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Authors:
Hardik B. Sailor, Hemant A. Patil
Submitted On:
31 March 2016 - 4:04am
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[1] Hardik B. Sailor, Hemant A. Patil, "FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1075. Accessed: Sep. 20, 2019.
@article{1075-16,
url = {http://sigport.org/1075},
author = {Hardik B. Sailor; Hemant A. Patil },
publisher = {IEEE SigPort},
title = {FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION},
year = {2016} }
TY - EJOUR
T1 - FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION
AU - Hardik B. Sailor; Hemant A. Patil
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1075
ER -
Hardik B. Sailor, Hemant A. Patil. (2016). FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/1075
Hardik B. Sailor, Hemant A. Patil, 2016. FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION. Available at: http://sigport.org/1075.
Hardik B. Sailor, Hemant A. Patil. (2016). "FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION." Web.
1. Hardik B. Sailor, Hemant A. Patil. FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1075

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