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20122022
most citedSelf-Attention Networks for Connectionist Temporal Classification in Speech Recognition

133 citations · 226 across the 12 of their papers we have counts for

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cs.CL2022

Device Directedness with Contextual Cues for Spoken Dialog Systems

Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati +2

In this work, we define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins. Fol…

cs.CL2022

Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting

Saket Dingliwal, Monica Sunkara, Sravan Bodapati +3

End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently. In these models, a non-autoregr…

cs.CL2021

Prompt-tuning in ASR systems for efficient domain-adaptation

Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3

Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains. Since domain-specific systems perform better than their…

cs.CL2021

Adapting Long Context NLM for ASR Rescoring in Conversational Agents

Ashish Shenoy, Sravan Bodapati, Monica Sunkara +2

Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language mod…

cs.CL2021

Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents

Ashish Shenoy, Sravan Bodapati, Katrin Kirchhoff

Goal-oriented conversational interfaces are designed to accomplish specific tasks and typically have interactions that tend to span multiple turns adhering to a pre-defined structu…

cs.CL2021

Neural Inverse Text Normalization

Monica Sunkara, Chaitanya Shivade, Sravan Bodapati +1

While there have been several contributions exploring state of the art techniques for text normalization, the problem of inverse text normalization (ITN) remains relatively unexplo…