Duration Modeling of Neural TTS for Automatic Dubbing
Author:
Affiliation:
1. Amazon AI
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747158.pdf?arnumber=9747158
Reference36 articles.
1. Duration Modeling For HMM-Based Speech Synthesis;yoshimura;ICSLP,1998
2. Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
3. Parallel Tacotron: Non-Autoregressive and Controllable TTS
4. Fastspeech 2: Fast and high-quality end-to-end text to speech;ren,2020
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1. Anim-400K: A Large-Scale Dataset for Automated End to End Dubbing of Video;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
2. Joint Multiscale Cross-Lingual Speaking Style Transfer With Bidirectional Attention Mechanism for Automatic Dubbing;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024
3. “I Won’t Go Speechless”: Design Exploration on a Real-Time Text-To-Speech Speaking Tool for Videoconferencing;Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19
4. Direct Speech Translation for Automatic Subtitling;Transactions of the Association for Computational Linguistics;2023
5. Prosodic alignment for off-screen automatic dubbing;Interspeech 2022;2022-09-18
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