Search Results for author: Anirudh Raju

Found 21 papers, 1 papers with code

MTL-SLT: Multi-Task Learning for Spoken Language Tasks

no code implementations NLP4ConvAI (ACL) 2022 Zhiqi Huang, Milind Rao, Anirudh Raju, Zhe Zhang, Bach Bui, Chul Lee

The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion

no code implementations26 Jan 2024 Jinhan Wang, Long Chen, Aparna Khare, Anirudh Raju, Pranav Dheram, Di He, Minhua Wu, Andreas Stolcke, Venkatesh Ravichandran

We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM).

Language Modelling Large Language Model

Federated Self-Learning with Weak Supervision for Speech Recognition

no code implementations21 Jun 2023 Milind Rao, Gopinath Chennupati, Gautam Tiwari, Anit Kumar Sahu, Anirudh Raju, Ariya Rastrow, Jasha Droppo

Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Cross-utterance ASR Rescoring with Graph-based Label Propagation

no code implementations27 Mar 2023 Srinath Tankasala, Long Chen, Andreas Stolcke, Anirudh Raju, Qianli Deng, Chander Chandak, Aparna Khare, Roland Maas, Venkatesh Ravichandran

We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity.

Fairness Language Modelling

Adaptive Endpointing with Deep Contextual Multi-armed Bandits

no code implementations23 Mar 2023 Do June Min, Andreas Stolcke, Anirudh Raju, Colin Vaz, Di He, Venkatesh Ravichandran, Viet Anh Trinh

In this paper, we aim to provide a solution for adaptive endpointing by proposing an efficient method for choosing an optimal endpointing configuration given utterance-level audio features in an online setting, while avoiding hyperparameter grid-search.

Multi-Armed Bandits

On joint training with interfaces for spoken language understanding

no code implementations30 Jun 2021 Anirudh Raju, Milind Rao, Gautam Tiwari, Pranav Dheram, Bryan Anderson, Zhe Zhang, Chul Lee, Bach Bui, Ariya Rastrow

Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding

no code implementations12 Feb 2021 Milind Rao, Pranav Dheram, Gautam Tiwari, Anirudh Raju, Jasha Droppo, Ariya Rastrow, Andreas Stolcke

Spoken language understanding (SLU) systems extract transcriptions, as well as semantics of intent or named entities from speech, and are essential components of voice activated systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Multi-task Language Modeling for Improving Speech Recognition of Rare Words

no code implementations23 Nov 2020 Chao-Han Huck Yang, Linda Liu, Ankur Gandhe, Yile Gu, Anirudh Raju, Denis Filimonov, Ivan Bulyko

We show that our rescoring model trained with these additional tasks outperforms the baseline rescoring model, trained with only the language modeling task, by 1. 4% on a general test and by 2. 6% on a rare word test set in terms of word-error-rate relative (WERR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces

no code implementations14 Aug 2020 Milind Rao, Anirudh Raju, Pranav Dheram, Bach Bui, Ariya Rastrow

Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces transcripts as well as NLU interpretation.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Scalable Multi Corpora Neural Language Models for ASR

no code implementations2 Jul 2019 Anirudh Raju, Denis Filimonov, Gautam Tiwari, Guitang Lan, Ariya Rastrow

Neural language models (NLM) have been shown to outperform conventional n-gram language models by a substantial margin in Automatic Speech Recognition (ASR) and other tasks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Data Augmentation for Robust Keyword Spotting under Playback Interference

no code implementations1 Aug 2018 Anirudh Raju, Sankaran Panchapagesan, Xing Liu, Arindam Mandal, Nikko Strom

Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.

Acoustic echo cancellation Data Augmentation +1

On Evaluating and Comparing Open Domain Dialog Systems

no code implementations11 Jan 2018 Anu Venkatesh, Chandra Khatri, Ashwin Ram, Fenfei Guo, Raefer Gabriel, Ashish Nagar, Rohit Prasad, Ming Cheng, Behnam Hedayatnia, Angeliki Metallinou, Rahul Goel, Shaohua Yang, Anirudh Raju

In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement.

Goal-Oriented Dialogue Systems Open-Domain Dialog

Topic-based Evaluation for Conversational Bots

1 code implementation11 Jan 2018 Fenfei Guo, Angeliki Metallinou, Chandra Khatri, Anirudh Raju, Anu Venkatesh, Ashwin Ram

Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined.

Topic Classification

Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting

no code implementations5 May 2017 Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Geng-Shen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni

Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.

Small-Footprint Keyword Spotting

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