We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-
modal model that takes advantage of pretrained foundational speech and language models …

In this work, we present a model-based approach to improving contextual biasing that
improves quality without drastically increasing model computation during inference …

ASR systems in real applications must be adapted on the fly to correctly recognize task-
specific contextual terms, such as contacts, application names and media entities. However …

Contextual Spelling Correction (CSC) models are used to improve automatic speech
recognition (ASR) quality given userspecific context. Typically, context is modeled as a large …

Adapting a robust automatic speech recognition (ASR) system to tackle unseen noise
scenarios is crucial. Integrating adapters into neural networks has emerged as a potent …

Contextual spelling correction models are an alternative to shallow fusion to improve
automatic speech recognition (ASR) quality given user vocabulary. To deal with large user …

Contextual biasing enables speech recognizers to transcribe important phrases in the
speaker's context, such as contact names, even if they are rare in, or absent from, the …

Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR)
systems towards rare entities that are relevant to the specific user or application scenarios …

Despite the impressive performance of ASR models on mainstream benchmarks, their
performance on rare words is unsatisfactory. In enterprise settings, often a focused list of …

Abstract Contextual Automatic Speech Recognition (ASR) requires scalable and accurate
retrieval of content relevant to the user's context. This paper presents a comparative study of …