4 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…
Although narrowband (NB) and wideband (WB) speech data primarily differ in sampling rate, these two common input sources are difficult to simultaneously model for automatic speech recognition (ASR). Meanwhile, cycle consistent generative adversarial networks (CycleGANs) have been shown value in a nu…
The performance of modern large vocabulary continuous speech recognition (LVCSR) systems is heavily affected by segment boundaries, proper speaker identification of the segments, as well as removal of spurious data. We propose to use Long Short Term Memory (LSTM) recurrent neural networks to partiti…