Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Zhang, Huayun"'
Speech evaluation measures a learners oral proficiency using automatic models. Corpora for training such models often pose sparsity challenges given that there often is limited scored data from teachers, in addition to the score distribution across p
Externí odkaz:
http://arxiv.org/abs/2409.14666
Current emotional text-to-speech (TTS) models predominantly conduct supervised training to learn the conversion from text and desired emotion to its emotional speech, focusing on a single emotion per text-speech pair. These models only learn the corr
Externí odkaz:
http://arxiv.org/abs/2409.10157
Current strategies for achieving fine-grained prosody control in speech synthesis entail extracting additional style embeddings or adopting more complex architectures. To enable zero-shot application of pretrained text-to-speech (TTS) models, we pres
Externí odkaz:
http://arxiv.org/abs/2408.06827
The standard Gaussian Process (GP) only considers a single output sample per input in the training set. Datasets for subjective tasks, such as spoken language assessment, may be annotated with output labels from multiple human raters per input. This
Externí odkaz:
http://arxiv.org/abs/2306.02719
Text-to-speech (TTS) models have achieved remarkable naturalness in recent years, yet like most deep neural models, they have more parameters than necessary. Sparse TTS models can improve on dense models via pruning and extra retraining, or converge
Externí odkaz:
http://arxiv.org/abs/2211.07283
Publikováno v:
Interspeech 2022, 823-827 (2022)
Neural models are known to be over-parameterized, and recent work has shown that sparse text-to-speech (TTS) models can outperform dense models. Although a plethora of sparse methods has been proposed for other domains, such methods have rarely been
Externí odkaz:
http://arxiv.org/abs/2209.10890
Speech evaluation is an essential component in computer-assisted language learning (CALL). While speech evaluation on English has been popular, automatic speech scoring on low resource languages remains challenging. Work in this area has focused on m
Externí odkaz:
http://arxiv.org/abs/2107.03675
Publikováno v:
Engineering Reports; Nov2024, Vol. 6 Issue 11, p1-18, 18p
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Akademický článek
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