Chander Chandak

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 contribute equally to better model accuracy.

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- and memory-efficient, especially for on-device applications, we propose end-to-end, streaming, joint ASR-LID architectures based on the recurrent neural network transducer framework.