5 accepted papers
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance. Conventionally, it is modeled as a speech-based language identification task. Prior techniques have been constrained to a single modality; however in the case of video data there i…
Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) --- languages for which NLP research is particularly far behind in meeting user needs --- it is feasible to annotate small amounts of data. Motivated by this, we pr…
Streaming end-to-end automatic speech recognition (ASR) models are widely used on smart speakers and on-device applications. Since these models are expected to transcribe speech with minimal latency, they are constrained to be causal with no future context, compared to their non-streaming counterpar…
Code-switching occurs when the speaker alternates between two or more languages or dialects. It is a pervasive phenomenon in most Indic spoken languages. Code-switching poses a challenge in language modeling as it complicates the orthographic realization of text, and generally, there is a shortage o…
Language Models (LMs) for Automatic Speech Recognition (ASR) can benefit from utilizing non-linguistic contextual signals in modeling. Examples of these signals include the geographical location of the user speaking to the system and/or the identity of the application (app) being spoken to. In pract…