5 accepted papers
Large Language Models (LLMs) demonstrate impressive capabilities, yet interaction with these models is mostly facilitated through text. Using Text-To-Speech to synthesize LLM outputs typically results in notable latency, which is impractical for fluent voice conversations. We propose LLM2Speech, an…
Autoregressive Attentive Sequence-to-Sequence (S2S) speech synthesis is considered state-of-the-art in terms of speech quality and naturalness, as evaluated on a finite set of testing utterances. However, it can occasionally suffer from stability issues at inference time, such as local intelligibili…
The generation of natural and expressive prosodic contours is an important component of a text-to-speech (TTS) system which, in most classical architectures, relies on the existence of a text-analysis processor that can extract prosody-predictive features and pass them to a statistical learning mode…
In this work we explore data-augmentation techniques for the task of improving the performance of a supervised recurrent-neural-network classifier tasked with predicting prosodic-boundary and pitch-accent labels. The technique is based on applying voice transformations to the training data that modi…
The prediction of symbolic prosodic categories from text is an important, but challenging, natural-language processing task given the various ways in which an input can be realized, and the fact that knowledge about what features determine this realization is incomplete or inaccessible to the model.…