no code implementations • 13 Jul 2022 • Lu Zeng, Sree Hari Krishnan Parthasarathi, Yuzong Liu, Alex Escott, Santosh Kumar Cheekatmalla, Nikko Strom, Shiv Vitaladevuni
We organize our results in two embedded chipset settings: a) with commodity ARM NEON instruction set and 8-bit containers, we present accuracy, CPU, and memory results using sub 8-bit weights (4, 5, 8-bit) and 8-bit quantization of rest of the network; b) with off-the-shelf neural network accelerators, for a range of weight bit widths (1 and 5-bit), while presenting accuracy results, we project reduction in memory utilization.
no code implementations • 13 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.
no code implementations • 13 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.
no code implementations • 9 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.
no code implementations • 5 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.