6 accepted papers
Automatic speech recognition (ASR) systems can benefit from incorporating contextual information to improve recognition accuracy, especially for uncommon words or phrases. Current approaches like custom vocabularies or prompting with previous transcript segments provide limited contextual control. C…
Faithfulness evaluators based on Large Language Models (LLMs) are often fooled by the fluency of the text and struggle with identifying errors in the summaries, usually leading to high false negative rate. We propose an approach to summary faithfulness evaluation in which multiple LLM-based agents a…
This work proposes a new parameter-efficient adaptation approach for multilingual ASR based on the hyper-network. Existing multilingual ASR adaptation methods apply either one residual adapter for all the languages, or language dependent adapters for each individual language. The residual adapter ca…
Spoken language identification (LID) has seen substantial performance gains with the rise of large-scale models. However, these models are often computationally expensive and impractical for many real-world applications. In this work, we propose a novel knowledge distillation from ensemble framework…
One common approach for question answering over speech data is to first transcribe speech using automatic speech recognition (ASR) and then employ text-based retrieval-augmented generation (RAG) on the transcriptions. While this cascaded pipeline has proven effective in many practical settings, ASR…
Despite recent advancements in speech processing, zero-resource speech translation (ST) and automatic speech recognition (ASR) remain challenging problems. In this work, we propose to leverage a multilingual Large Language Model (LLM) to perform ST and ASR in languages for which the model has never…