Papers

Length-Normalized Representation Learning for Speech Signals

International Journal
2021~
작성자
dsp
작성일
2022-06-08 16:07
조회
1298
Authors : Kyungguen Byun, Seyun 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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341 International Conference Jihyun Kim, Hong-Goo Kang "Contrastive Learning based Deep Latent Masking for Music Source Seperation" in INTERSPEECH, 2023
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339 International Conference Hyungchan Yoon, Seyun Um, Changhwan Kim, Hong-Goo Kang "Adversarial Learning of Intermediate Acoustic Feature for End-to-End Lightweight Text-to-Speech" in INTERSPEECH, 2023
338 International Conference Hyungchan Yoon, Changhwan Kim, Eunwoo Song, Hyun-Wook Yoon, Hong-Goo Kang "Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech" in INTERSPEECH, 2023
337 International Conference Doyeon Kim, Soo-Whan Chung, Hyewon Han, Youna Ji, Hong-Goo Kang "HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders" in INTERSPEECH, 2023
336 Domestic Conference Jihyun Lee, Wootaek Lim, Hong-Goo Kang "음성 압축에서의 심층 신경망 기반 장구간 예측" in 한국방송·미디어공학회 2023년 하계학술대회, 2023