A huge amount of research has been done in the field of speech signal processing in recent
years. In particular, there has been increasing interest in the automatic speech recognition …

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 …

Automatic speech recognition (ASR) systems have dramatically improved over the last few
years. ASR systems are most often trained from'typical'speech, which means that …

Current state-of-the-art automatic speech recognition systems are trained to work in
specificdomains', defined based on factors like application, sampling rate and codec. When …

We study the effectiveness of several techniques to personalize end-to-end speech models
and improve the recognition of proper names relevant to the user. These techniques differ in …

Speaker-independent speech recognition systems trained with data from many users are
generally robust against speaker variability and work well for a large population of speakers …

Speech data from different domains has distinct acoustic and linguistic characteristics. It is
common to train a single multidomain model such as a Conformer transducer for speech …

In the absence of large-scale in-domain supervised training data, ASR models can achieve
reasonable performance through pre-training on additional data that is unlabeled …

A recent paradigm shift in artificial intelligence has seen the rise of foundation models, such
as the large language models and the universal speech models. With billions of model …

The cross-domain performance of automatic speech recognition (ASR) could be severely
hampered due to the mismatch between training and testing distributions. Since the target …