6 accepted papers
Human speech exhibits rich and flexible prosodic variations. To address the one-to-many mapping problem from text to prosody in a reasonable and flexible manner, we propose DiffStyleTTS, a multi-speaker acoustic model based on a conditional diffusion module and an improved classifier-free guidance,…
This paper investigates leveraging large-scale speech data to enhance prosodic modeling in speech synthesis, and introduces a model named SP2MC which achieves self-supervised prosody learning at phoneme-level with momentum contrast. This model incorporates dual convolutional encoders for speech and…
Conversational speech synthesis aims to synthesize speech of an individual speaker based on history conversation. However, most studies in conversational speech synthesis only focus on the synthesis performance of the current speaker’s turn and neglect the temporal relationship between turns of inte…
To address the issue of one-to-many mapping from phoneme sequences to acoustic features in expressive speech synthesis, this paper proposes a method of discourse-level prosody modeling with a variational autoencoder (VAE) based on the non-autoregressive architecture of FastSpeech. In this method, ph…
This paper presents a method of detecting Alzheimer’s disease (AD) from the spontaneous speech of subjects in a picture description task using neural networks. This method does not rely on the manual transcriptions and annotations of a subject’s speech, but utilizes the bottleneck features extracted…
Data scarcity is always a constraint on analyzing speech transcriptions for automatic Alzheimer’s disease (AD) detection, especially when the subjects are non-English speakers. To deal with this issue, this paper first proposes a contrastive learning method to obtain effective representations for te…