Search Results for author: Shiv Vitaladevuni

Found 5 papers, 0 papers with code

Towards Data-efficient Modeling for Wake Word Spotting

no code implementations13 Oct 2020 Yixin Gao, Yuriy Mishchenko, Anish Shah, Spyros Matsoukas, Shiv Vitaladevuni

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.

Data Augmentation

On Front-end Gain Invariant Modeling for Wake Word Spotting

no code implementations13 Oct 2020 Yixin Gao, Noah D. Stein, Chieh-Chi Kao, Yunliang Cai, Ming Sun, Tao Zhang, Shiv Vitaladevuni

Since the WW model is trained with the AFE-processed audio data, its performance is sensitive to AFE variations, such as gain changes.

Accurate Detection of Wake Word Start and End Using a CNN

no code implementations9 Aug 2020 Christin Jose, Yuriy Mishchenko, Thibaud Senechal, Anish Shah, Alex Escott, Shiv Vitaladevuni

In this paper, we propose two new methods for detecting the endpoints of wake words in neural KWS that use single-stage word-level neural networks.

voice assistant

Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting

no code implementations5 May 2017 Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Geng-Shen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni

Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.

Small-Footprint Keyword Spotting

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