Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

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 …

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

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
improving performance and data efficiency on various speech tasks. However, these …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
producing performance and data efficiency improvements for a variety of speech tasks …

Acoustic word embeddings (AWEs) are fixed-dimensional representations of variable-length
speech segments. For zero-resource languages where labelled data is not available, one …

Acoustic word embeddings (AWEs) are vector representations of spoken word segments.
AWEs can be learned jointly with embeddings of character sequences, to generate …

Acoustic word embeddings are fixed-dimensional representations of variable-length speech
segments. Such embeddings can form the basis for speech search, indexing and discovery …