Search Results for author: Saket Dingliwal

Found 8 papers, 4 papers with code

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

no code implementations18 Oct 2022 Saket Dingliwal, Monica Sunkara, Sravan Bodapati, Srikanth Ronanki, Jeff Farris, Katrin Kirchhoff

End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently.

speech-recognition Speech Recognition

Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems

no code implementations16 Dec 2021 Saket Dingliwal, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff

Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead.

Automatic Speech Recognition Domain Adaptation +1

Prompt-tuning in ASR systems for efficient domain-adaptation

no code implementations13 Oct 2021 Saket Dingliwal, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff

In this work, we overcome the problem using prompt-tuning, a methodology that trains a small number of domain token embedding parameters to prime a transformer-based LM to a particular domain.

Automatic Speech Recognition Domain Adaptation +1

Few Shot Dialogue State Tracking using Meta-learning

1 code implementation EACL 2021 Saket Dingliwal, Bill Gao, Sanchit Agarwal, Chien-Wei Lin, Tagyoung Chung, Dilek Hakkani-Tur

Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc.

Chatbot Dialogue State Tracking +1

Covariate Distribution Aware Meta-learning

1 code implementation ICML Workshop LifelongML 2020 Amrith Setlur, Saket Dingliwal, Barnabas Poczos

Based on this model we propose a computationally feasible meta-learning algorithm by introducing meaningful relaxations in our final objective.

Few-Shot Learning regression

Finding Input Characterizations for Output Properties in ReLU Neural Networks

1 code implementation9 Mar 2020 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.

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