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- W3198664020 abstract "With the technology development, more and more IoT devices with displays are making face-to-face interaction through visualization a reality. To protect the privacy of users, communications can be represented through avatars and use audio-driven real-time speech animation. However, if audio is the only available input, the quality of the outcome relies heavily on real-time phoneme recognition, such as recognition accuracy and latency. This paper introduces a novel deep-learning-based real-time phoneme recognition network (RealPRNet) scheme to leverage spatial and temporal patterns in the input audio data. With featured long short-term memory stack block and long short-term features, RealPRNet can achieve super performance in phoneme recognition. Our comprehensive empirical results show that compared to the state-of-the-art algorithms, RealPRNet can achieve 20% phoneme error rate (PER) improvement and 4% block error distance (BDE) improvement in the best case." @default.
- W3198664020 created "2021-09-13" @default.
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- W3198664020 date "2021-01-01" @default.
- W3198664020 modified "2023-10-18" @default.
- W3198664020 title "RealPRNet: A Real-time Phoneme Recognized Network for “Believable Speech Animation" @default.
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- W3198664020 doi "https://doi.org/10.1109/jiot.2021.3110468" @default.
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