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This paper delves into the emerging field of face-based voice conversion, leveraging the unique relationship between an individual's facial features and their vocal characteristics. We present a novel face-based voice conversion framework that partic
Externí odkaz:
http://arxiv.org/abs/2408.09802
There has been growing interest in audio-language retrieval research, where the objective is to establish the correlation between audio and text modalities. However, most audio-text paired datasets often lack rich expression of the text data compared
Externí odkaz:
http://arxiv.org/abs/2405.00367
Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existing emotional speech synthesis models have shown controllability using
Externí odkaz:
http://arxiv.org/abs/2211.06160
Autor:
Kim, Eungbeom, Kim, Jinhee, Oh, Yoori, Kim, Kyungsu, Park, Minju, Sim, Jaeheon, Lee, Jinwoo, Lee, Kyogu
In this paper, we aim to unveil the impact of data augmentation in audio-language multi-modal learning, which has not been explored despite its importance. We explore various augmentation methods at not only train-time but also test-time and find out
Externí odkaz:
http://arxiv.org/abs/2210.17143
Akademický článek
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