The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Nowadays speech synthesis or text to speech (TTS), an ability of system to produce human
like natural sounding voice from the written text, is gaining popularity in the field of speech …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

Neural network based end-to-end text to speech (TTS) has significantly improved the quality
of synthesized speech. Prominent methods (eg, Tacotron 2) usually first generate mel …

Self-attention is a useful mechanism to build generative models for language and images. It
determines the importance of context elements by comparing each element to the current …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the
efficiency by parallel decoding without sacrificing the quality. However, existing NAT …

The Transformer architecture consists of self-attention and feed-forward networks (FFNs)
which can be viewed as key-value memories according to previous works. However, FFN …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Error correction techniques have been used to refine the output sentences from automatic
speech recognition (ASR) models and achieve a lower word error rate (WER) than original …