371 citations · 974 across the 26 of their papers we have counts for
31 papers
Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters
Anshu Bhatia, Sanchit Sinha, Saket Dingliwal +3
Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks. However, such repr…
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR
Goeric Huybrechts, Srikanth Ronanki, Xilai Li +3
Conformer-based end-to-end models have become ubiquitous these days and are commonly used in both streaming and non-streaming automatic speech recognition (ASR). Techniques like du…
Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation
Nilaksh Das, Monica Sunkara, Sravan Bodapati +4
End-to-end ASR models trained on large amount of data tend to be implicitly biased towards language semantics of the training data. Internal language model estimation (ILME) has be…
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale
Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal +3
Language models have been shown to perform better with an increase in scale on a wide variety of tasks via the in-context learning paradigm. In this paper, we investigate the hypot…
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…
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…