OD-SLA-2: Speech Enhancement
Wed, 15 Dec, 14:40 - 16:40 Japan Standard Time (UTC +9)
Wed, 15 Dec, 05:40 - 07:40 Coordinated Universal Time
Wed, 15 Dec, 00:40 - 02:40 Eastern Standard Time (UTC -5)
Tue, 14 Dec, 21:40 - 23:40 Pacific Standard Time (UTC -8)
Track: Speech, Language, and Audio (SLA)

OD-SLA-2.1: CycleGAN-based Non-parallel Speech Enhancement with an Adaptive Attention-in-attention Mechanism

Guochen Yu, Yutian Wang, Communication University of China, China; Chengshi Zheng, Institute of Acoustics, Chinese Academy of Sciences, China; Hui Wang, Qin Zhang, Communication University of China, China

OD-SLA-2.2: A Robust Maximum Likelihood Distortionless Response Beamformer based on a Complex Generalized Gaussian Distribution

Weixin Meng, Chengshi Zheng, Xiaodong Li, Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Science, Beijing, 100190, China, China

OD-SLA-2.4: DNN-BASED LINEAR PREDICTION RESIDUAL ENHANCEMENT FOR SPEECH DEREVERBERATION

Xinyang Feng, Nuo Li, Zunwen He, Yan Zhang, Wancheng Zhang, Beijing Institute of Technology, China

OD-SLA-2.5: Mandarin Electro-Laryngeal Speech Enhancement based on Statistical Voice Conversion and Manual Tone Control

Zhaopeng Qian, Haijun Niu, Beihang University, China; Li Wang, Beijing Research Center of Urban System Engineering, China; Kazuhiro Kobayashi, Nagoya University, Japan; Shaochuan Zhang, Beihang University, China; Tomoki Toda, Nagoya University, Japan

OD-SLA-2.6: Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization

Lu Zhang, Mingjiang Wang, Harbin Institute of Technology, Shenzhen, China; Andong Li, Institute of Acoustics, Chinese Academy of Sciences, China; Zehua Zhang, Xuyi Zhuang, Harbin Institute of Technology, Shenzhen, China

OD-SLA-2.7: Low-Power Convolutional Recurrent Neural Network For Monaural Speech Enhancement

Fei Gao, Haixin Guan, Unisound AI Technology Co. Ltd, China

OD-SLA-2.8: Multi-channel Speech Enhancement with 2-D Convolutional Time-frequency Domain Features and a Pre-trained Acoustic Model

Quandong Wang, Junnan Wu, Zhao Yan, Sichong Qian, Liyong Guo, Lichun Fan, Weiji Zhuang, Peng Gao, Yujun Wang, Xiaomi Corporation, China

OD-SLA-2.9: Processing Phoneme Specific Segments for Cleft Lip and Palate Speech Enhancement

Protima Nomo Sudro, Rohit Sinha, Indian Institute of Technology Guwahati, India; S R Mahadeva Prasanna, Indian Institute of Technology Dharwad, India

OD-SLA-2.10: Speech Enhancement by Noise Self-Supervised Rank-Constrained Spatial Covariance Matrix Estimation via Independent Deeply Learned Matrix Analysis

Sota Misawa, Norihiro Takamune, Tomohiko Nakamura, The University of Tokyo, Japan; Daichi Kitamura, National Institute of Technology, Japan; Hiroshi Saruwatari, The University of Tokyo, Japan; Masakazu Une, University of Tsukuba, Japan; Shoji Makino, Waseda University, Japan

OD-SLA-2.11: CAUSAL DISTORTIONLESS RESPONSE BEAMFORMING BY ALTERNATING DIRECTION METHOD OF MULTIPLIERS

Yoshiki Masuyama, Kouei Yamaoka, Yuma Kinoshita, Nobutaka Ono, Tokyo Metropolitan University‚Äč, Japan

OD-SLA-2.12: STACKED U-NET WITH HIGH-LEVEL FEATURE TRANSFER FOR PARAMETER EFFICIENT SPEECH ENHANCEMENT

Jinyoung Lee, Hong-Goo Kang, Yonsei University, Korea, Republic of

OD-SLA-2.13: EXTENSION OF VIRTUAL MICROPHONE TECHNIQUE TO MULTIPLE REAL MICROPHONES AND INVESTIGATION OF THE IMPACT OF PHASE AND AMPLITUDE INTERPOLATION ON SPEECH ENHANCEMENT

Hanako Segawa, University of Tsukuba, Japan; Li Li, NTT Corporation, Japan; Shoji Makino, University of Tsukuba, Waseda University, Japan; Takeshi Yamada, University of Tsukuba, Japan

OD-SLA-2.14: Comparative Study on DNN-based Minimum Variance Beamforming Robust to Small Movements of Sound Sources

Kohei Saijo, Waseda University, Japan; Kazuhiro Katagiri, Masaru Fujieda, OKI Electric Industry Corporation, Japan; Tetsunori Kobayashi, Tetsuji Ogawa, Waseda University, Japan

OD-SLA-2.15: IMPROVEMENTS TO NON-INTRUSIVE INTELLIGIBILITY PREDICTION FOR REVERBERANT SPEECH

Kazushi Nakazawa, Kazuhiro Kondo, Yamagata University, Japan