End-to-end models with large capacity have significantly improved multilingual automatic
speech recognition, but their computation cost poses challenges for on-device applications …

We investigate using large language models (LLMs) to generate text-only training data for
improving multilingual and code-switching automatic speech recognition (ASR) through a …

Multilingual end-to-end automatic speech recognition models are attractive due to its
simplicity in training and deployment. Recent work on large-scale training of such models …

To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more
unpaired text data by multi-modal training, one needs to address two problems: 1) the …

Multilingual automatic speech recognition (ASR) systems have garnered attention for their
potential to extend language coverage globally. While self-supervised learning (SSL) …

Automatic speech recognition (ASR) systems need to be accurate, have low latency, and
effectively handle language switching in order to be useful for the 60% of the world …

The number of end-to-end speech recognition models grows every year. These models are
often adapted to new domains or languages resulting in a proliferation of expert systems that …

ASR model deployment environment is ever-changing, and the incoming speech can be
switched across different domains during a session. This brings a challenge for effective …

The end-to-end ASR model is often desired in the streaming multilingual scenario since it is
easier to deploy and can benefit from pre-trained speech models such as powerful …

One of limitations in end-to-end automatic speech recognition (ASR) framework is its
performance would be compromised if train-test utterance lengths are mismatched. In this …