The integration of Language Models (LMs) has proven to be an effective way to address
domain shifts in speech recognition. However, these approaches usually require a …

End-to-end (E2E) automatic speech recognition (ASR) implicitly learns the token sequence
distribution of paired audio-transcript training data. However, it still suffers from domain shifts …

Neural transducers provide a natural approach to streaming ASR. However, they augment
output sequences with blank tokens which leads to challenges for domain adaptation using …

Collecting audio-text pairs is expensive; however, it is much easier to access text-only data.
Unless using shallow fusion, end-to-end automatic speech recognition (ASR) models …

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great
success by jointly learning acoustic and linguistic information, it still suffers from the effect of …

In this work, we propose a method to create domain-sensitive speech recognition models
that utilize textual domain information by conditioning its generation on a given text prompt …

Although end-to-end (E2E) automatic speech recognition (ASR) has shown state-of-the-art
recognition accuracy, it tends to be implicitly biased towards the training data distribution …

The attention-based encoder-decoder (AED) speech recognition model has been widely
successful in recent years. However, the joint optimization of acoustic model and language …

Recently, connectionist temporal classification (CTC)-based end-to-end (E2E) automatic
speech recognition (ASR) models have achieved impressive results, especially with the …

Automatic speech recognition (ASR) systems have emerged as indispensable tools across a
wide spectrum of applications, ranging from transcription services to voice-activated …