Search Results for author: Anirudh Raju

Found 14 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 Multi-Task Learning +2

End-to-End Spoken Language Understanding using RNN-Transducer ASR

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

We propose an end-to-end trained spoken language understanding (SLU) system that extracts transcripts, intents and slots from an input speech utterance.

Automatic Speech Recognition Natural Language Understanding +1

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 Natural Language Understanding +1

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 Multi-Task Learning

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 Natural Language Understanding +1

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

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

Contextual Language Model Adaptation for Conversational Agents

no code implementations26 Jun 2018 Anirudh Raju, Behnam Hedayatnia, Linda Liu, Ankur Gandhe, Chandra Khatri, Angeliki Metallinou, Anu Venkatesh, Ariya Rastrow

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.

Automatic Speech Recognition

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

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

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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