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112 results on '"Shengkui Zhao"'

48. Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses

Author

Trung Hieu Nguyen, Bin Ma, and Shengkui Zhao

Subjects
FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Computer science, Speech recognition, Computer Science - Sound, Machine Learning (cs.LG), Time–frequency analysis, Speech enhancement, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Time domain, Representation (mathematics), Joint (audio engineering), Encoder, Decoding methods, Block (data storage), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Deep complex U-Net structure and convolutional recurrent network (CRN) structure achieve state-of-the-art performance for monaural speech enhancement. Both deep complex U-Net and CRN are encoder and decoder structures with skip connections, which heavily rely on the representation power of the complex-valued convolutional layers. In this paper, we propose a complex convolutional block attention module (CCBAM) to boost the representation power of the complex-valued convolutional layers by constructing more informative features. The CCBAM is a lightweight and general module which can be easily integrated into any complex-valued convolutional layers. We integrate CCBAM with the deep complex U-Net and CRN to enhance their performance for speech enhancement. We further propose a mixed loss function to jointly optimize the complex models in both time-frequency (TF) domain and time domain. By integrating CCBAM and the mixed loss, we form a new end-to-end (E2E) complex speech enhancement framework. Ablation experiments and objective evaluations show the superior performance of the proposed approaches., 5 pages, 4 figures, 2 tables, accepted by ICASSP 2021

Published
2021

50. Towards Natural Bilingual and Code-Switched Speech Synthesis Based on Mix of Monolingual Recordings and Cross-Lingual Voice Conversion

Author

Hao Wang, Shengkui Zhao, Trung Hieu Nguyen, and Bin Ma

Subjects
FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Computer science, Speech recognition, Speech synthesis, Intelligibility (communication), computer.software_genre, Mandarin Chinese, language.human_language, Computer Science - Sound, Machine Learning (cs.LG), Fluency, Naturalness, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Code (cryptography), language, Natural (music), computer, Transformer (machine learning model), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recent state-of-the-art neural text-to-speech (TTS) synthesis models have dramatically improved intelligibility and naturalness of generated speech from text. However, building a good bilingual or code-switched TTS for a particular voice is still a challenge. The main reason is that it is not easy to obtain a bilingual corpus from a speaker who achieves native-level fluency in both languages. In this paper, we explore the use of Mandarin speech recordings from a Mandarin speaker, and English speech recordings from another English speaker to build high-quality bilingual and code-switched TTS for both speakers. A Tacotron2-based cross-lingual voice conversion system is employed to generate the Mandarin speaker's English speech and the English speaker's Mandarin speech, which show good naturalness and speaker similarity. The obtained bilingual data are then augmented with code-switched utterances synthesized using a Transformer model. With these data, three neural TTS models -- Tacotron2, Transformer and FastSpeech are applied for building bilingual and code-switched TTS. Subjective evaluation results show that all the three systems can produce (near-)native-level speech in both languages for each of the speaker., 5 pages, 2 figures, INTERSPEECH 2020

Published
2020

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