no code implementations • 5 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.
1 code implementation • 11 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.
no code implementations • 11 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.
no code implementations • 26 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 Automatic Speech Recognition (ASR) +2
no code implementations • 1 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.
no code implementations • 5 Jan 2019 • Ladislav Mošner, Minhua Wu, Anirudh Raju, Sree Hari Krishnan Parthasarathi, Kenichi Kumatani, Shiva Sundaram, Roland Maas, Björn Hoffmeister
For real-world speech recognition applications, noise robustness is still a challenge.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 2 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
no code implementations • 14 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
no code implementations • 23 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) +3
no code implementations • 12 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
no code implementations • 14 May 2021 • Swayambhu Nath Ray, Minhua Wu, Anirudh Raju, Pegah Ghahremani, Raghavendra Bilgi, Milind Rao, Harish Arsikere, Ariya Rastrow, Andreas Stolcke, Jasha Droppo
On the other hand, a streaming system using per-frame intent posteriors as extra inputs for the RNN-T ASR system yields a 3. 33% relative WERR.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 30 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
no code implementations • 13 Dec 2021 • Kai Wei, Thanh Tran, Feng-Ju Chang, Kanthashree Mysore Sathyendra, Thejaswi Muniyappa, Jing Liu, Anirudh Raju, Ross McGowan, Nathan Susanj, Ariya Rastrow, Grant P. Strimel
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding Spoken Language Understanding
no code implementations • 19 Jul 2022 • Gopinath Chennupati, Milind Rao, Gurpreet Chadha, Aaron Eakin, Anirudh Raju, Gautam Tiwari, Anit Kumar Sahu, Ariya Rastrow, Jasha Droppo, Andy Oberlin, Buddha Nandanoor, Prahalad Venkataramanan, Zheng Wu, Pankaj Sitpure
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 22 Jul 2022 • Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju, I-Fan Chen, Brian King, Katherine Powell, Melissa Saboowala, Karan Shetty, Andreas Stolcke
As for other forms of AI, speech recognition has recently been examined with respect to performance disparities across different user cohorts.
no code implementations • 23 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.
no code implementations • 27 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.
no code implementations • 21 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
no code implementations • 17 Jan 2024 • Anirudh Raju, Aparna Khare, Di He, Ilya Sklyar, Long Chen, Sam Alptekin, Viet Anh Trinh, Zhe Zhang, Colin Vaz, Venkatesh Ravichandran, Roland Maas, Ariya Rastrow
Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands.
no code implementations • 26 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).
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