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pro vyhledávání: '"Chandra, Shreeram Suresh"'
Synthesizing the voices of unseen speakers is a persisting challenge in multi-speaker text-to-speech (TTS). Most multi-speaker TTS models rely on modeling speaker characteristics through speaker conditioning during training. Modeling unseen speaker a
Externí odkaz:
http://arxiv.org/abs/2408.17432
Autor:
Salman, Ali N., Du, Zongyang, Chandra, Shreeram Suresh, Ulgen, Ismail Rasim, Busso, Carlos, Sisman, Berrak
Voice conversion (VC) research traditionally depends on scripted or acted speech, which lacks the natural spontaneity of real-life conversations. While natural speech data is limited for VC, our study focuses on filling in this gap. We introduce a no
Externí odkaz:
http://arxiv.org/abs/2406.04494
Recent advances in style transfer text-to-speech (TTS) have improved the expressiveness of synthesized speech. However, encoding stylistic information (e.g., timbre, emotion, and prosody) from diverse and unseen reference speech remains a challenge.
Externí odkaz:
http://arxiv.org/abs/2406.03637
Many frameworks for emotional text-to-speech (E-TTS) rely on human-annotated emotion labels that are often inaccurate and difficult to obtain. Learning emotional prosody implicitly presents a tough challenge due to the subjective nature of emotions.
Externí odkaz:
http://arxiv.org/abs/2405.11413
In this paper, we present a survey on the utility of machine learning (ML) algorithms for applications in cognitive radio networks (CRN). We start with a high-level overview of some of the major challenges in CRNs, and mention the ML architectures an
Externí odkaz:
http://arxiv.org/abs/2106.10413