Papers

Length-Normalized Representation Learning for Speech Signals

International Journal
2021~
작성자
dsp
작성일
2022-06-08 16:07
조회
51
Authors : Kyungguen Byun, Se-yun Um, Hong-Goo Kang

Year : 2022

Publisher / Conference : IEEE Access

Volume : 10

Page : 60362-60372

Research area : Speech Signal Processing, Text-to-Speech, Speech Recognition

Presentation/Publication date : 2022.06.08

Presentation : None

In this study, we proposed a length-normalized representation learning method for speech and text to address the inherent problem of sequence-to-sequence models when the input and output sequences exhibit different lengths. To this end, the representations were constrained to a fixed-length shape by including length normalization and de-normalization processes in the pre- and post-network architecture of the transformer-based self-supervised learning framework. Consequently, this enabled the direct modelling of the relationships between sequences with different length without attention or recurrent network between representation domains. This method not only achieved the aforementioned regularized length effect but also achieved a data augmentation effect that effectively handled differently time-scaled input features. The performance of the proposed length-normalized representations on downstream tasks for speaker and phoneme recognition was investigated to verify the effectiveness of this method over conventional representation methods. In addition, to demonstrate the applicability of the proposed representation method to sequence-to-sequence modeling, a unified speech recognition and text-to-speech (TTS) system was developed. The unified system achieved a high accuracy on a frame-wise phoneme prediction and exhibited a promising potential for the generation of high-quality synthesized speech signals on the TTS.
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326 International Conference Hyeon-Kyeong Shin, Hyewon Han, Doyeon Kim, Soo-Whan Chung, Hong-Goo Kang "Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting" in INTERSPEECH, 2022
325 International Conference Changhwan Kim, Se-yun Um, Hyungchan Yoon, Hong-goo Kang "FluentTTS: Text-dependent Fine-grained Style Control for Multi-style TTS" in INTERSPEECH, 2022
324 International Conference Miseul Kim, Zhenyu Piao, Seyun Um, Ran Lee, Jaemin Joh, Seungshin Lee, Hong-Goo Kang "Light-Weight Speaker Verification with Global Context Information" in INTERSPEECH, 2022
323 International Journal Kyungguen Byun, Se-yun Um, Hong-Goo Kang "Length-Normalized Representation Learning for Speech Signals" in IEEE Access, vol.10, pp.60362-60372, 2022
322 International Conference Doyeon Kim, Hyewon Han, Hyeon-Kyeong Shin, Soo-Whan Chung, Hong-Goo Kang "Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement" in ICASSP, 2022
321 International Conference Chanwoo Lee, Hyungseob Lim, Jihyun Lee, Inseon Jang, Hong-Goo Kang "Progressive Multi-Stage Neural Audio Coding with Guided References" in ICASSP, 2022
320 International Conference Jihyun Lee, Hyungseob Lim, Chanwoo Lee, Inseon Jang, Hong-Goo Kang "Adversarial Audio Synthesis Using a Harmonic-Percussive Discriminator" in ICASSP, 2022
319 International Conference Jinyoung Lee and Hong-Goo Kang "Stacked U-Net with High-level Feature Transfer for Parameter Efficient Speech Enhancement" in APSIPA ASC, 2021
318 International Conference Huu-Kim Nguyen, Kihyuk Jeong, Se-Yun Um, Min-Jae Hwang, Eunwoo Song, Hong-Goo Kang "LiteTTS: A Decoder-free Light-weight Text-to-wave Synthesis Based on Generative Adversarial Networks" in INTERSPEECH, 2021
317 International Conference Zainab Alhakeem, Yoohwan Kwon, Hong-Goo Kang "Disentangled Representations for Arabic Dialect Identification based on Supervised Clustering with Triplet Loss" in EUSIPCO, 2021