activity
20202022
most citedAccurate Detection of Wake Word Start and End Using a CNN

30 citations · 34 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SD2022

Wakeword Detection under Distribution Shifts

Sree Hari Krishnan Parthasarathi, Lu Zeng, Christin Jose +1

We propose a novel approach for semi-supervised learning (SSL) designed to overcome distribution shifts between training and real-world data arising in the keyword spotting (KWS) t…

eess.AS2022★ 3 cited

Latency Control for Keyword Spotting

Christin Jose, Joseph Wang, Grant P. Strimel +3

Conversational agents commonly utilize keyword spotting (KWS) to initiate voice interaction with the user. For user experience and privacy considerations, existing approaches to KW…

cs.LG2021

Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting

Mohammad Omar Khursheed, Christin Jose, Rajath Kumar +3

In this work, we propose Tiny-CRNN (Tiny Convolutional Recurrent Neural Network) models applied to the problem of wakeword detection, and augment them with scaled dot product atten…

eess.AS2020★ 1 cited

Small Footprint Convolutional Recurrent Networks for Streaming Wakeword Detection

Mohammad Omar Khursheed, Christin Jose, Rajath Kumar +3

In this work, we propose small footprint Convolutional Recurrent Neural Network models applied to the problem of wakeword detection and augment them with scaled dot product attenti…

eess.AS2020★ 30 cited

Accurate Detection of Wake Word Start and End Using a CNN

Christin Jose, Yuriy Mishchenko, Thibaud Senechal +3

Small footprint embedded devices require keyword spotters (KWS) with small model size and detection latency for enabling voice assistants. Such a keyword is often referred to as \t…