11 citations · 11 across the 2 of their papers we have counts for
10 papers
Towards Data-efficient Modeling for Wake Word Spotting
Yixin Gao, Yuriy Mishchenko, Anish Shah +2
Wake word (WW) spotting is challenging in far-field not only because of the interference in signal transmission but also the complexity in acoustic environments. Traditional WW mod…
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Sunghyun Park, Han Li, Ameen Patel +5
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding…
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based…
Semi-supervised Acoustic Event Detection based on tri-training
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, wh…
Parsing Coordination for Spoken Language Understanding
Sanchit Agarwal, Rahul Goel, Tagyoung Chung +3
Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology. The parses contain intents and slots that are directly consumed by do…
Active Learning for New Domains in Natural Language Understanding
Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha +2
We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an…