2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDCHLG-3.2
Paper Title THE THINKIT SYSTEM FOR ICASSP2021 M2VOC CHALLENGE
Authors Zengqiang Shang, Haozhe Zhang, Ziyi Chen, Bolin Zhou, Pengyuan Zhang, University of Chinese Academy of Sciences, China
SessionCHLG-3: Multi-Speaker Multi-Style Voice Cloning Challenge (M2VoC)
LocationZoom
Session Time:Monday, 07 June, 15:30 - 17:45
Presentation Time:Monday, 07 June, 15:30 - 17:45
Presentation Poster
Topic Grand Challenge: Multi-Speaker Multi-Style Voice Cloning Challenge (M2VoC)
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we introduce the low resource text-to-speech system from the ThinkIT team submitted to Multi-Speaker Multi-Style Voice Cloning Challenge (M2VoC). The challenge has two tasks: few-shot track1 provides 100 samples for each person and one-shot track2 offers 5 samples only. Each track contains two sub-tracks A and B. Instead of sub-track A, sub-track B can use extra public data besides the released data. But we participate in the sub-track A only. We choose the finetune as our backbone strategy. Our submitted systems include BERT based prosody boundary prediction module, FastSpeech based acoustic model to generate acoustic features from text input, and HIFIGAN based vocoder to generate waveform from acoustic features. Among them, acoustic models are susceptible to low resource speakers. To prevent over-fitting, we modified the acoustic model and split out validation set to assist the manual model selection. Evaluation results provided by the challenges organizers demonstrate the effectiveness of our system.