41 citations · 43 across the 2 of their papers we have counts for
9 papers
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…