85 citations · 285 across the 20 of their papers we have counts for
20 papers
Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding
Kumari Nishu, Minsik Cho, Paul Dixon +1
Spotting user-defined/flexible keywords represented in text frequently uses an expensive text encoder for joint analysis with an audio encoder in an embedding space, which can suff…
Matching Latent Encoding for Audio-Text based Keyword Spotting
Kumari Nishu, Minsik Cho, Devang Naik
Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word…
PDP: Parameter-free Differentiable Pruning is All You Need
Minsik Cho, Saurabh Adya, Devang Naik
DNN pruning is a popular way to reduce the size of a model, improve the inference latency, and minimize the power consumption on DNN accelerators. However, existing approaches migh…
R2 Loss: Range Restriction Loss for Model Compression and Quantization
Arnav Kundu, Chungkuk Yoo, Srijan Mishra +2
Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable acc…
HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words
Arnav Kundu, Mohammad Samragh Razlighi, Minsik Cho +2
Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be effici…
I see what you hear: a vision-inspired method to localize words
Mohammad Samragh, Arnav Kundu, Ting-Yao Hu +5
This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contempora…