3 accepted papers
We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts…
The development of large datasets for various tasks has driven the success of deep learning models but at the cost of increased label noise, duplication, collection challenges, storage capabilities, and training requirements. In this work, we investigate whether all samples in large datasets contrib…
Conventional dynamic language switching enables seamless multilingual interactions by running several monolingual ASR systems in parallel and triggering the appropriate downstream components using a standalone language identification (LID) service. Since this solution is neither scalable nor cost- a…