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Speech Synthesis and Generation, including TTS (SPE-SYNT)

HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK

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Authors:
Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba
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17 June 2018 - 4:42am
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poster.pdf

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[1] Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba, "HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3233. Accessed: Aug. 21, 2018.
@article{3233-18,
url = {http://sigport.org/3233},
author = {Fuming Fang; Junichi Yamagishi; Isao Echizen; Jaime Lorenzo-Trueba },
publisher = {IEEE SigPort},
title = {HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK
AU - Fuming Fang; Junichi Yamagishi; Isao Echizen; Jaime Lorenzo-Trueba
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3233
ER -
Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba. (2018). HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK. IEEE SigPort. http://sigport.org/3233
Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba, 2018. HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK. Available at: http://sigport.org/3233.
Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba. (2018). "HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK." Web.
1. Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba. HIGH-QUALITY NONPARALLEL VOICE CONVERSION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3233

Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody


We describe a new application of deep-learning-based speech synthesis, namely multilingual speech synthesis for generating controllable foreign accent. Specifically, we train a DBLSTM-based acoustic model on non-accented multilingual speech recordings from a speaker native in several languages. By copying durations and pitch contours from a pre-recorded utterance of the desired prompt, natural prosody is achieved. We call this paradigm "cyborg speech" as it combines human and machine speech parameters.

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Authors:
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi
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29 April 2018 - 1:59pm
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Cyborg Speech presentation slides

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[1] Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi, "Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3187. Accessed: Aug. 21, 2018.
@article{3187-18,
url = {http://sigport.org/3187},
author = {Jaime Lorenzo-Trueba; Mariko Kondo; Junichi Yamagishi },
publisher = {IEEE SigPort},
title = {Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody},
year = {2018} }
TY - EJOUR
T1 - Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody
AU - Jaime Lorenzo-Trueba; Mariko Kondo; Junichi Yamagishi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3187
ER -
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi. (2018). Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody. IEEE SigPort. http://sigport.org/3187
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi, 2018. Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody. Available at: http://sigport.org/3187.
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi. (2018). "Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody." Web.
1. Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi. Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3187

An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features


Although a WaveNet vocoder can synthesize more natural-sounding speech waveforms than conventional vocoders with sampling frequencies of 16 and 24 kHz, it is difficult to directly extend the sampling frequency to 48 kHz to cover the entire human audible frequency range for higher-quality synthesis because the model size becomes too large to train with a consumer GPU. For a WaveNet vocoder with a sampling frequency of 48 kHz with a consumer GPU, this paper introduces a subband WaveNet architecture to a speaker-dependent WaveNet vocoder and proposes a subband WaveNet vocoder.

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Authors:
Takuma Okamoto, Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai
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24 April 2018 - 2:34am
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ICASSP_2018_subband_WaveNet_vocoder.pdf

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[1] Takuma Okamoto, Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai, "An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3162. Accessed: Aug. 21, 2018.
@article{3162-18,
url = {http://sigport.org/3162},
author = {Takuma Okamoto; Kentaro Tachibana; Tomoki Toda; Yoshinori Shiga; Hisashi Kawai },
publisher = {IEEE SigPort},
title = {An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features},
year = {2018} }
TY - EJOUR
T1 - An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features
AU - Takuma Okamoto; Kentaro Tachibana; Tomoki Toda; Yoshinori Shiga; Hisashi Kawai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3162
ER -
Takuma Okamoto, Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai. (2018). An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features. IEEE SigPort. http://sigport.org/3162
Takuma Okamoto, Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai, 2018. An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features. Available at: http://sigport.org/3162.
Takuma Okamoto, Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai. (2018). "An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features." Web.
1. Takuma Okamoto, Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai. An investigation of subband WaveNet vocoder covering entire audible frequency range with limited acoustic features [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3162

NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS

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17 April 2018 - 8:46pm
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ICASSP 2018 - Tacotron 2.pdf

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[1] , "NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2953. Accessed: Aug. 21, 2018.
@article{2953-18,
url = {http://sigport.org/2953},
author = { },
publisher = {IEEE SigPort},
title = {NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS},
year = {2018} }
TY - EJOUR
T1 - NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2953
ER -
. (2018). NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS. IEEE SigPort. http://sigport.org/2953
, 2018. NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS. Available at: http://sigport.org/2953.
. (2018). "NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS." Web.
1. . NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2953

TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS

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Authors:
Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
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17 April 2018 - 4:50pm
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saito18icassp_tts.pdf

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[1] Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari, "TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2946. Accessed: Aug. 21, 2018.
@article{2946-18,
url = {http://sigport.org/2946},
author = {Yuki Saito; Shinnosuke Takamichi; Hiroshi Saruwatari },
publisher = {IEEE SigPort},
title = {TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS
AU - Yuki Saito; Shinnosuke Takamichi; Hiroshi Saruwatari
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2946
ER -
Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari. (2018). TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/2946
Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari, 2018. TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/2946.
Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari. (2018). "TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS." Web.
1. Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari. TEXT-TO-SPEECH SYNTHESIS USING STFT SPECTRA BASED ON LOW-/MULTI-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2946

NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS

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Authors:
Yuki Saito, Yusuke Ijima, Kyosuke Nishida, Shinnosuke Takamichi
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17 April 2018 - 4:47pm
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saito18icassp_vc_v2.pdf

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[1] Yuki Saito, Yusuke Ijima, Kyosuke Nishida, Shinnosuke Takamichi, "NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2945. Accessed: Aug. 21, 2018.
@article{2945-18,
url = {http://sigport.org/2945},
author = {Yuki Saito; Yusuke Ijima; Kyosuke Nishida; Shinnosuke Takamichi },
publisher = {IEEE SigPort},
title = {NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS},
year = {2018} }
TY - EJOUR
T1 - NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS
AU - Yuki Saito; Yusuke Ijima; Kyosuke Nishida; Shinnosuke Takamichi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2945
ER -
Yuki Saito, Yusuke Ijima, Kyosuke Nishida, Shinnosuke Takamichi. (2018). NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS. IEEE SigPort. http://sigport.org/2945
Yuki Saito, Yusuke Ijima, Kyosuke Nishida, Shinnosuke Takamichi, 2018. NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS. Available at: http://sigport.org/2945.
Yuki Saito, Yusuke Ijima, Kyosuke Nishida, Shinnosuke Takamichi. (2018). "NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS." Web.
1. Yuki Saito, Yusuke Ijima, Kyosuke Nishida, Shinnosuke Takamichi. NON-PARALLEL VOICE CONVERSION USING VARIATIONAL AUTOENCODERS CONDITIONED BY PHONETIC POSTERIORGRAMS AND D-VECTORS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2945

On the use of WaveNet as a Statistical Vocoder

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17 April 2018 - 5:40am
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WaveNet_Vocoder_Poster_4cols_v2.pdf

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[1] , "On the use of WaveNet as a Statistical Vocoder", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2931. Accessed: Aug. 21, 2018.
@article{2931-18,
url = {http://sigport.org/2931},
author = { },
publisher = {IEEE SigPort},
title = {On the use of WaveNet as a Statistical Vocoder},
year = {2018} }
TY - EJOUR
T1 - On the use of WaveNet as a Statistical Vocoder
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2931
ER -
. (2018). On the use of WaveNet as a Statistical Vocoder. IEEE SigPort. http://sigport.org/2931
, 2018. On the use of WaveNet as a Statistical Vocoder. Available at: http://sigport.org/2931.
. (2018). "On the use of WaveNet as a Statistical Vocoder." Web.
1. . On the use of WaveNet as a Statistical Vocoder [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2931

An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation


We propose a noise shaping method to improve the sound quality of speech signals generated by WaveNet, which is a convolutional neural network (CNN) that predicts a waveform sample sequence as a discrete symbol sequence. Speech signals generated by WaveNet often suffer from noise signals caused by the quantization error generated by representing waveform samples as discrete symbols and the prediction error of the CNN.

ICASSP2018_NS.pdf

PDF icon Poster pdf (168 downloads)

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Authors:
Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai
Submitted On:
15 April 2018 - 1:02am
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[1] Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai, "An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2880. Accessed: Aug. 21, 2018.
@article{2880-18,
url = {http://sigport.org/2880},
author = {Kentaro Tachibana; Tomoki Toda; Yoshinori Shiga; Hisashi Kawai },
publisher = {IEEE SigPort},
title = {An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation},
year = {2018} }
TY - EJOUR
T1 - An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation
AU - Kentaro Tachibana; Tomoki Toda; Yoshinori Shiga; Hisashi Kawai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2880
ER -
Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai. (2018). An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation. IEEE SigPort. http://sigport.org/2880
Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai, 2018. An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation. Available at: http://sigport.org/2880.
Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai. (2018). "An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation." Web.
1. Kentaro Tachibana, Tomoki Toda, Yoshinori Shiga, Hisashi Kawai. An Investigation of Noise Shaping with Perceptual Weighting for WaveNet-based Speech Generation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2880

On the analysis of training data for wavenet-based speech synthesis


In this paper, we analyze how much, how consistent and how accurate data WaveNet-based speech synthesis method needs to be abletogeneratespeechofgoodquality. Wedothisbyaddingartificial noise to the description of our training data and observing how well WaveNet trains and produces speech. More specifically, we add noise to both phonetic segmentation and annotation accuracy, and we also reduce the size of training data by using a fewer number of sentences during training of a WaveNet model.

poster.pdf

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Authors:
Zdeněk Hanzlíček, Jindřich Matoušek
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13 April 2018 - 4:16pm
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poster.pdf

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[1] Zdeněk Hanzlíček, Jindřich Matoušek, "On the analysis of training data for wavenet-based speech synthesis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2759. Accessed: Aug. 21, 2018.
@article{2759-18,
url = {http://sigport.org/2759},
author = {Zdeněk Hanzlíček; Jindřich Matoušek },
publisher = {IEEE SigPort},
title = {On the analysis of training data for wavenet-based speech synthesis},
year = {2018} }
TY - EJOUR
T1 - On the analysis of training data for wavenet-based speech synthesis
AU - Zdeněk Hanzlíček; Jindřich Matoušek
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2759
ER -
Zdeněk Hanzlíček, Jindřich Matoušek. (2018). On the analysis of training data for wavenet-based speech synthesis. IEEE SigPort. http://sigport.org/2759
Zdeněk Hanzlíček, Jindřich Matoušek, 2018. On the analysis of training data for wavenet-based speech synthesis. Available at: http://sigport.org/2759.
Zdeněk Hanzlíček, Jindřich Matoušek. (2018). "On the analysis of training data for wavenet-based speech synthesis." Web.
1. Zdeněk Hanzlíček, Jindřich Matoušek. On the analysis of training data for wavenet-based speech synthesis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2759

MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM


This paper proposes a novel noise compensation algorithm for a glottal excitation model in a deep learning (DL)-based speech synthesis system.
To generate high-quality speech synthesis outputs, the balance between harmonic and noise components of the glottal excitation signal should be well-represented by the DL network.
However, it is hard to accurately model the noise component because the DL training process inevitably results in statistically smoothed outputs; thus, it is essential to introduce an additional noise compensation process.

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13 April 2018 - 1:15am
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ICASSP2018_MbG_glottal.pdf

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ICASSP2018_MbG_glottal.pdf

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[1] , "MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2602. Accessed: Aug. 21, 2018.
@article{2602-18,
url = {http://sigport.org/2602},
author = { },
publisher = {IEEE SigPort},
title = {MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM},
year = {2018} }
TY - EJOUR
T1 - MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2602
ER -
. (2018). MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM. IEEE SigPort. http://sigport.org/2602
, 2018. MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM. Available at: http://sigport.org/2602.
. (2018). "MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM." Web.
1. . MODELING-BY-GENERATION-STRUCTURED NOISE COMPENSATION ALGORITHM FOR GLOTTAL VOCODING SPEECH SYNTHESIS SYSTEM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2602

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