77 citations · 237 across the 65 of their papers we have counts for
10 papers · 1 filter
Speech Enhancement with Zero-Shot Model Selection
Ryandhimas E. Zezario, Chiou-Shann Fuh, Hsin-Min Wang +1
Recent research on speech enhancement (SE) has seen the emergence of deep-learning-based methods. It is still a challenging task to determine the effective ways to increase the gen…
STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model
Ryandhimas E. Zezario, Szu-Wei Fu, Chiou-Shann Fuh +2
The calculation of most objective speech intelligibility assessment metrics requires clean speech as a reference. Such a requirement may limit the applicability of these metrics in…
The Academia Sinica Systems of Voice Conversion for VCC2020
Yu-Huai Peng, Cheng-Hung Hu, Alexander Kang +4
This paper describes the Academia Sinica systems for the two tasks of Voice Conversion Challenge 2020, namely voice conversion within the same language (Task 1) and cross-lingual v…
Melody Harmonization Using Orderless NADE, Chord Balancing, and Blocked Gibbs Sampling
Chung-En Sun, Yi-Wei Chen, Hung-Shin Lee +2
Coherence and interestingness are two criteria for evaluating the performance of melody harmonization, which aims to generate a chord progression from a symbolic melody. In this st…
Improved Lite Audio-Visual Speech Enhancement
Shang-Yi Chuang, Hsin-Min Wang, Yu Tsao
Numerous studies have investigated the effectiveness of audio-visual multimodal learning for speech enhancement (AVSE) tasks, seeking a solution that uses visual data as auxiliary…
Lite Audio-Visual Speech Enhancement
Shang-Yi Chuang, Yu Tsao, Chen-Chou Lo +1
Previous studies have confirmed the effectiveness of incorporating visual information into speech enhancement (SE) systems. Despite improved denoising performance, two problems may…
SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning
Chi-Chang Lee, Yu-Chen Lin, Hsuan-Tien Lin +2
Numerous noise adaptation techniques have been proposed to fine-tune deep-learning models in speech enhancement (SE) for mismatched noise environments. Nevertheless, adaptation to…
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
Tsun-An Hsieh, Hsin-Min Wang, Xugang Lu +1
Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, t…
Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion
Wen-Chin Huang, Hao Luo, Hsin-Te Hwang +4
An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE)…
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
Cheng Yu, Ryandhimas E. Zezario, Syu-Siang Wang +5
Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model gener…