Search Results for author: Nikko Strom

Found 6 papers, 1 papers with code

Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets

no code implementations13 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.

Keyword Spotting Quantization

Lessons from Building Acoustic Models with a Million Hours of Speech

no code implementations2 Apr 2019 Sree Hari Krishnan Parthasarathi, Nikko Strom

This is a report of our lessons learned building acoustic models from 1 Million hours of unlabeled speech, while labeled speech is restricted to 7, 000 hours.

Data Augmentation for Robust Keyword Spotting under Playback Interference

no code implementations1 Aug 2018 Anirudh Raju, Sankaran Panchapagesan, Xing Liu, Arindam Mandal, Nikko Strom

Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.

Acoustic echo cancellation Data Augmentation +1

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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