1 citations · 2 across the 4 of their papers we have counts for
9 papers
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…
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…
Few Shot Dialogue State Tracking using Meta-learning
Saket Dingliwal, Bill Gao, Sanchit Agarwal +3
Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc. With the…
Robust Handwriting Recognition with Limited and Noisy Data
Hai Pham, Amrith Setlur, Saket Dingliwal +7
Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that…
Covariate Distribution Aware Meta-learning
Amrith Setlur, Saket Dingliwal, Barnabas Poczos
Meta-learning has proven to be successful for few-shot learning across the regression, classification, and reinforcement learning paradigms. Recent approaches have adopted Bayesian…
Finding Input Characterizations for Output Properties in ReLU Neural Networks
Saket Dingliwal, Divyansh Pareek, Jatin Arora
Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are being increasingly deployed in real-world safety-critical domains. Despite the widespread success, their co…