We introduce a new cross-modal fusion technique designed for generative error correction
in automatic speech recognition (ASR). Our methodology leverages both acoustic …

End-to-end (E2E) automatic speech recognition (ASR) systems often have difficulty
recognizing uncommon words, that appear infrequently in the training data. One promising …

We propose a neural language modeling system based on low-rank adaptation (LoRA) for
speech recognition output rescoring. Although pretrained language models (LMs) like BERT …

In collaborative learning environments, effective intelligent learning systems need to
accurately analyze and understand the collaborative discourse between learners (ie, group …

The recognition of personalized content, such as contact names, remains a challenging
problem for end-to-end speech recognition systems. In this work, we demonstrate how first …

Natural Language Interfaces that facilitate data exploration tasks are rapidly gaining in
interest in the research community because they enable users to focus their attention on the …

Live ML analytics have gained increasing popularity with large-scale deployments due to
recent evolution of ML technologies. To serve live ML queries, experts nowadays still need …

End-to-end automatic speech recognition (ASR) systems are increasingly popular due to
their relative architectural simplicity and competitive performance. However, even though the …

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 …

Retrieval is a widely adopted approach for improving language models leveraging external
information. As the field moves towards multi-modal large language models, it is important to …