Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Recently proposed self-supervised learning approaches have been successful for pre-
training speech representation models. The utility of these learned representations has been …

Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …

The classification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Although deep learning has shown proven advantages …

In this paper, we explore neural network models that learn to associate segments of spoken
audio captions with the semantically relevant portions of natural images that they refer to …

We compare self-supervised representation learning algorithms which either explicitly
quantize the audio data or learn representations without quantization. We find the former to …

Even in the absence of any explicit semantic annotation, vast collections of audio recordings
provide valuable information for learning the categorical structure of sounds. We consider …

We present a simple approach to improve direct speech-to-text translation (ST) when the
source language is low-resource: we pre-train the model on a high-resource automatic …

Abstract The Interspeech 2015 Zero Resource Speech Challenge aims at discovering
subword and word units from raw speech. The challenge provides the first unified and open …

Recent studies have been revisiting whole words as the basic modelling unit in speech
recognition and query applications, instead of phonetic units. Such whole-word segmental …