Search Results for author: Dhanush Bekal

Found 5 papers, 1 papers with code

SpeechVerse: A Large-scale Generalizable Audio Language Model

no code implementations14 May 2024 Nilaksh Das, Saket Dingliwal, Srikanth Ronanki, Rohit Paturi, David Huang, Prashant Mathur, Jie Yuan, Dhanush Bekal, Xing Niu, Sai Muralidhar Jayanthi, Xilai Li, Karel Mundnich, Monica Sunkara, Sundararajan Srinivasan, Kyu J Han, Katrin Kirchhoff

The models are instruction finetuned using continuous latent representations extracted from the speech foundation model to achieve optimal zero-shot performance on a diverse range of speech processing tasks using natural language instructions.

Automatic Speech Recognition Benchmarking +4

Device Directedness with Contextual Cues for Spoken Dialog Systems

no code implementations23 Nov 2022 Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati, Srikanth Ronanki, Katrin Kirchhoff

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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Remember the context! ASR slot error correction through memorization

no code implementations10 Sep 2021 Dhanush Bekal, Ashish Shenoy, Monica Sunkara, Sravan Bodapati, Katrin Kirchhoff

Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech

no code implementations3 Aug 2020 Monica Sunkara, Srikanth Ronanki, Dhanush Bekal, Sravan Bodapati, Katrin Kirchhoff

Experiments conducted on the Fisher corpus show that our proposed approach achieves ~6-9% and ~3-4% absolute improvement (F1 score) over the baseline BLSTM model on reference transcripts and ASR outputs respectively.

Data Augmentation

Text Generation from Knowledge Graphs with Graph Transformers

3 code implementations NAACL 2019 Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.

Decoder Dialogue Generation +3

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