Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Despite the rapid progress of automatic speech recognition (ASR) technologies in the past
few decades, recognition of disordered speech remains a highly challenging task to date …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Five years after the first published proofs of concept, direct approaches to speech translation
(ST) are now competing with traditional cascade solutions. In light of this steady progress …

Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it
presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and …

It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition
models are competitive with hybrid models only when a large amount of data, at least a …

Multilingual speech recognition with supervised learning has achieved great results as
reflected in recent research. With the development of pretraining methods on audio and text …

Fake speech detection aims to distinguish fake speech from natural speech. This paper
presents an effective fake speech detection scheme based on residual network with …

The RNN transducer is a promising end-to-end model candidate. We compare the original
training criterion with the full marginalization over all alignments, to the commonly used …

Gender bias is largely recognized as a problematic phenomenon affecting language
technologies, with recent studies underscoring that it might surface differently across …