Papers

Efficient deep neural networks for speech synthesis using bottleneck features

International Conference
2016~2020
작성자
한혜원
작성일
2016-12-01 16:29
조회
1331
Authors : Young-Sun Joo, Won-Suk Jun, Hong-Goo Kang

Year : 2016

Publisher / Conference : APSIPA

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.
전체 355
102 International Conference Min-Jae Hwang, Hong-Goo Kang "Parameter enhancement for MELP speech codec in noisy communication environment" in INTERSPEECH, 2019
101 International Conference Keulbit Kim, Jinkyu Lee, Jan Skoglund, Hong-Goo Kang "Model Order Selection for Wind Noise Reduction in Non-negative Matrix Factorization" in ITC-CSCC, 2019
100 International Conference Ohsung Kwon, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "Emotional Speech Synthesis Based on Style Embedded Tacotron2 Framework" in ITC-CSCC, 2019
99 International Conference Kyungguen Byun, Eunwoo Song, Jinseob Kim, Jae-Min Kim, Hong-Goo Kang "Excitation-by-SampleRNN Model for Text-to-Speech" in ITC-CSCC, 2019
98 International Conference Yang Yuan, Soo-Whan Chung, Hong-Goo Kang "Gradient-based active learning query strategy for end-to-end speech recognition" in ICASSP, 2019
97 International Conference Soo-Whan Chung, Joon Son Chung, Hong-Goo Kang "Perfect match: Improved cross-modal embeddings for audio-visual synchronisation" in ICASSP, 2019
96 International Conference Hyewon Han, Kyungguen Byun, Hong-Goo Kang "A Deep Learning-based Stress Detection Algorithm with Speech Signal" in Workshop on Audio-Visual Scene Understanding for Immersive Multimedia (AVSU’18), 2018
95 International Conference Min-Jae Hwang, Eunwoo Song, Jin-Seob Kim, Hong-Goo Kang "A Unified Framework for the Generation of Glottal Signals in Deep Learning-based Parametric Speech Synthesis Systems" in INTERSPEECH, 2018
94 International Conference Haemin Yang, Soyeon Choe, Keulbit Kim, Hong-Goo Kang "Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement" in ICSigSys, 2018
93 International Conference Min-Jae Hwang, Eunwoo Song, Kyungguen Byun, Hong-Goo Kang "Modeling-by-Generation-Structured Noise Compensation Algorithm for Glottal Vocoding Speech Synthesis System" in ICASSP, 2018