We present Maestro, a self-supervised training method to unify representations learnt from
speech and text modalities. Self-supervised learning from speech signals aims to learn the …

How can speech-to-text translation (ST) perform as well as machine translation (MT)? The
key point is to bridge the modality gap between speech and text so that useful MT …

End-to-end speech summarization (E2E SSum) is a technique to directly generate summary
sentences from speech. Compared with the cascade approach, which combines automatic …

Currently, a common approach in many speech processing tasks is to leverage large scale
pre-trained models by fine-tuning them on in-domain data for a particular application. Yet …

We propose an end-to-end Automatic Speech Recognition (ASR) system that can be trained
on transcribed speech data, text-only data, or a mixture of both. The proposed model uses …

In this work we evaluate the utility of synthetic data for training automatic speech recognition
(ASR). We use the ASR training data to train a text-to-speech (TTS) system similar to …

Abstract Automatic Speech Recognition is among the most important areas of Artificial
Intelligence research today. One of the most notable advances in this area is the …

While the deep learning revolution has led to significant performance improvements in
speech recognition, accented speech remains a challenge. Current approaches to this …

This paper presents a method for selecting appropriate synthetic speech samples from a
given large text-to-speech (TTS) dataset as supplementary training data for an automatic …

This paper presents a novel methodology for enhancing Automatic Speech Recognition
(ASR) performance by utilizing contrastive learning to filter synthetic audio data. We address …