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

Superficially, read and spontaneous speech—the two main kinds of training data for
automatic speech recognition—appear as complementary, but are equal: pairs of texts and …

Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have
shown promising results in various downstream tasks in the speech community. In particular …

Self-attention (SA) is a critical component of Transformer neural networks that have
succeeded in automatic speech recognition (ASR). In this paper, we analyze the role of SA …

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 …

Self-supervised learning (SSL) is seen as a very promising approach with high performance
for several speech downstream tasks. Since the parameters of SSL models are generally so …

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 …

Large, pre-trained neural networks consisting of self-attention layers (transformers) have
recently achieved state-of-the-art results on several speech emotion recognition (SER) …

Alzheimer's dementia is a progressive neurodegenerative disease that causes cognitive and
physical impairment. It severely deteriorates the quality of life in affected individuals. An …

Recent self-supervised learning (SSL) models have proven to learn rich representations of
speech, which can readily be utilized by diverse downstream tasks. To understand such …