Efficient deep neural networks for speech synthesis using bottleneck features

Young Sun Joo; Won Suk Jun; Hong Goo Kang

doi:10.1109/APSIPA.2016.7820721

Efficient deep neural networks for speech synthesis using bottleneck features

Young Sun Joo, Won Suk Jun, Hong Goo Kang

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper proposes a cascading deep neural network (DNN) structure for speech synthesis system that consists of text-to-bottleneck (TTB) and bottleneck-to-speech (BTS) models. Unlike conventional single structure that requires a large database to find complicated mapping rules between linguistic and acoustic features, the proposed structure is very effective even if the available training database is inadequate. The bottle-neck feature utilized in the proposed approach represents the characteristics of linguistic features and its average acoustic features of several speakers. Therefore, it is more efficient to learn a mapping rule between bottleneck and acoustic features than to learn directly a mapping rule between linguistic and acoustic features. Experimental results show that the learning capability of the proposed structure is much higher than that of the conventional structures. Objective and subjective listening test results also verify the superiority of the proposed structure.

Original language	English
Title of host publication	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9789881476821
DOIs	https://doi.org/10.1109/APSIPA.2016.7820721
Publication status	Published - 2017 Jan 17
Event	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of Duration: 2016 Dec 13 → 2016 Dec 16

Publication series

Name	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country/Territory	Korea, Republic of
City	Jeju
Period	16/12/13 → 16/12/16

Bibliographical note

Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Information Systems
Signal Processing

Access to Document

10.1109/APSIPA.2016.7820721

Cite this

Joo, Y. S., Jun, W. S., & Kang, H. G. (2017). Efficient deep neural networks for speech synthesis using bottleneck features. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820721] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820721

Joo, Young Sun ; Jun, Won Suk ; Kang, Hong Goo. / Efficient deep neural networks for speech synthesis using bottleneck features. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

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abstract = "This paper proposes a cascading deep neural network (DNN) structure for speech synthesis system that consists of text-to-bottleneck (TTB) and bottleneck-to-speech (BTS) models. Unlike conventional single structure that requires a large database to find complicated mapping rules between linguistic and acoustic features, the proposed structure is very effective even if the available training database is inadequate. The bottle-neck feature utilized in the proposed approach represents the characteristics of linguistic features and its average acoustic features of several speakers. Therefore, it is more efficient to learn a mapping rule between bottleneck and acoustic features than to learn directly a mapping rule between linguistic and acoustic features. Experimental results show that the learning capability of the proposed structure is much higher than that of the conventional structures. Objective and subjective listening test results also verify the superiority of the proposed structure.",

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Joo, YS, Jun, WS & Kang, HG 2017, Efficient deep neural networks for speech synthesis using bottleneck features. in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016., 7820721, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Jeju, Korea, Republic of, 16/12/13. https://doi.org/10.1109/APSIPA.2016.7820721

Efficient deep neural networks for speech synthesis using bottleneck features. / Joo, Young Sun; Jun, Won Suk; Kang, Hong Goo.

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820721 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Efficient deep neural networks for speech synthesis using bottleneck features

AU - Joo, Young Sun

AU - Jun, Won Suk

AU - Kang, Hong Goo

PY - 2017/1/17

Y1 - 2017/1/17

N2 - This paper proposes a cascading deep neural network (DNN) structure for speech synthesis system that consists of text-to-bottleneck (TTB) and bottleneck-to-speech (BTS) models. Unlike conventional single structure that requires a large database to find complicated mapping rules between linguistic and acoustic features, the proposed structure is very effective even if the available training database is inadequate. The bottle-neck feature utilized in the proposed approach represents the characteristics of linguistic features and its average acoustic features of several speakers. Therefore, it is more efficient to learn a mapping rule between bottleneck and acoustic features than to learn directly a mapping rule between linguistic and acoustic features. Experimental results show that the learning capability of the proposed structure is much higher than that of the conventional structures. Objective and subjective listening test results also verify the superiority of the proposed structure.

AB - This paper proposes a cascading deep neural network (DNN) structure for speech synthesis system that consists of text-to-bottleneck (TTB) and bottleneck-to-speech (BTS) models. Unlike conventional single structure that requires a large database to find complicated mapping rules between linguistic and acoustic features, the proposed structure is very effective even if the available training database is inadequate. The bottle-neck feature utilized in the proposed approach represents the characteristics of linguistic features and its average acoustic features of several speakers. Therefore, it is more efficient to learn a mapping rule between bottleneck and acoustic features than to learn directly a mapping rule between linguistic and acoustic features. Experimental results show that the learning capability of the proposed structure is much higher than that of the conventional structures. Objective and subjective listening test results also verify the superiority of the proposed structure.

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M3 - Conference contribution

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Joo YS, Jun WS, Kang HG. Efficient deep neural networks for speech synthesis using bottleneck features. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820721. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). doi: 10.1109/APSIPA.2016.7820721

Efficient deep neural networks for speech synthesis using bottleneck features

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