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pro vyhledávání: '"Mallela, Jhansi"'
Automatic syllable stress detection is a crucial component in Computer-Assisted Language Learning (CALL) systems for language learners. Current stress detection models are typically trained on clean speech, which may not be robust in real-world scena
Externí odkaz:
http://arxiv.org/abs/2412.08306
Autor:
Mondal, Anindita, Bharadwaj, Rangavajjala Sankara, Mallela, Jhansi, Vuppala, Anil Kumar, Yarra, Chiranjeevi
Automatic detection of prominence at the word and syllable-levels is critical for building computer-assisted language learning systems. It has been shown that prosody embeddings learned by the current state-of-the-art (SOTA) text-to-speech (TTS) syst
Externí odkaz:
http://arxiv.org/abs/2412.08283
Autor:
Alexey Karpov, K. Samudravijaya, K. T. Deepak, Rajesh M. Hegde, Shyam S. Agrawal, S. R. Mahadeva Prasanna
The two-volume proceedings set LNAI 14338 and 14339 constitutes the refereed proceedings of the 25th International Conference on Speech and Computer, SPECOM 2023, held in Dharwad, India, during November 29–December 2, 2023.The 94 papers included i