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

Research on speech processing has traditionally considered the task of designing hand-
engineered acoustic features (feature engineering) as a separate distinct problem from the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic
speech recognition (ASR) coverage of the world's languages. They have shown …

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

Recently, there is increasing interest in multilingual automatic speech recognition (ASR)
where a speech recognition system caters to multiple low resource languages by taking …

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 …

Self-supervised speech representation learning (speech SSL) has demonstrated the benefit
of scale in learning rich representations for Automatic Speech Recognition (ASR) with …

Conventional keyword search (KWS) systems for speech databases match the input text
query to the set of word hypotheses generated by an automatic speech recognition (ASR) …