We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Recent progress in self-training, self-supervised pretraining and unsupervised learning
enabled well performing speech recognition systems without any labeled data. However, in …

Multilingual models can improve language processing, particularly for low resource
situations, by sharing parameters across languages. Multilingual acoustic models, however …

Despite the impressive performance recently achieved by automatic speech recognition
(ASR), we observe two primary challenges that hinder its broader applications:(1) The …

Existing speech to speech translation systems heavily rely on the text of target language:
they usually translate source language either to target text and then synthesize target …

Abstract Grapheme-to-Phoneme (G2P) has many applications in NLP and speech fields.
Most existing work focuses heavily on languages with abundant training datasets, which …

Abstract Automatic Speech Recognition systems have become essential for an independent
automation during the present-day era. A hybrid approach for Automatic Speech …

There is growing interest in building phone recognition systems for low-resource languages
as the majority of languages do not have any writing systems. Phone recognition systems …

In realistic speech enhancement settings for end-user devices, we often encounter only a
few speakers and noise types that tend to reoccur in the specific acoustic environment. We …

Building language-universal speech recognition systems entails producing phonological
units of spoken sound that can be shared across languages. While speech annotations at …