87 citations · 285 across the 18 of their papers we have counts for
20 papers
Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion
Jinhan Wang, Long Chen, Aparna Khare +6
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).…
Two-pass Endpoint Detection for Speech Recognition
Anirudh Raju, Aparna Khare, Di He +9
Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accur…
Federated Self-Learning with Weak Supervision for Speech Recognition
Milind Rao, Gopinath Chennupati, Gautam Tiwari +4
Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy. We study the problem…
Cross-utterance ASR Rescoring with Graph-based Label Propagation
Srinath Tankasala, Long Chen, Andreas Stolcke +6
We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional ne…
Adaptive Endpointing with Deep Contextual Multi-armed Bandits
Do June Min, Andreas Stolcke, Anirudh Raju +4
Current endpointing (EP) solutions learn in a supervised framework, which does not allow the model to incorporate feedback and improve in an online setting. Also, it is a common pr…
Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities
Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju +6
As for other forms of AI, speech recognition has recently been examined with respect to performance disparities across different user cohorts. One approach to achieve fairness in s…
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
Gopinath Chennupati, Milind Rao, Gurpreet Chadha +11
Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of hum…
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Kai Wei, Thanh Tran, Feng-Ju Chang +8
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. While dialo…
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End
Swayambhu Nath Ray, Minhua Wu, Anirudh Raju +7
Comprehending the overall intent of an utterance helps a listener recognize the individual words spoken. Inspired by this fact, we perform a novel study of the impact of explicitly…
On joint training with interfaces for spoken language understanding
Anirudh Raju, Milind Rao, Gautam Tiwari +6
Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances. SLU systems usually consist…
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding
Milind Rao, Pranav Dheram, Gautam Tiwari +4
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 sy…
Multi-task Language Modeling for Improving Speech Recognition of Rare Words
Chao-Han Huck Yang, Linda Liu, Ankur Gandhe +4
End-to-end automatic speech recognition (ASR) systems are increasingly popular due to their relative architectural simplicity and competitive performance. However, even though the…
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces
Milind Rao, Anirudh Raju, Pranav Dheram +2
We consider the problem of spoken language understanding (SLU) of extracting natural language intents and associated slot arguments or named entities from speech that is primarily…
Scalable Multi Corpora Neural Language Models for ASR
Anirudh Raju, Denis Filimonov, Gautam Tiwari +2
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. There…
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning
Ladislav Mošner, Minhua Wu, Anirudh Raju +5
For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean an…
Data Augmentation for Robust Keyword Spotting under Playback Interference
Anirudh Raju, Sankaran Panchapagesan, Xing Liu +2
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. It is…
Contextual Language Model Adaptation for Conversational Agents
Anirudh Raju, Behnam Hedayatnia, Linda Liu +5
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under…
Topic-based Evaluation for Conversational Bots
Fenfei Guo, Angeliki Metallinou, Chandra Khatri +3
Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using t…
On Evaluating and Comparing Open Domain Dialog Systems
Anu Venkatesh, Chandra Khatri, Ashwin Ram +10
Conversational agents are exploding in popularity. However, much work remains in the area of non goal-oriented conversations, despite significant growth in research interest over r…
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
Ming Sun, Anirudh Raju, George Tucker +6
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requi…