Journal
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Volume -, Issue -, Pages 7953-7957Publisher
IEEE
DOI: 10.1109/icassp.2019.8683669
Keywords
silicon cochlea spikes; event-driven auditory processing; DNN; keyword spotting; speaker verification
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Funding
- European Union's Horizon 2020 research and innovation program [644732]
- Swiss National Science Foundation [200021_172553]
- Swiss National Science Foundation (SNF) [200021_172553] Funding Source: Swiss National Science Foundation (SNF)
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This work presents an event-driven acoustic sensor processing pipeline to power a low-resource voice-activated smart assistant. The pipeline includes four major steps; namely localization, source separation, keyword spotting (KWS) and speaker verification (SV). The pipeline is driven by a front-end binaural spiking silicon cochlea sensor. The timing information carried by the output spikes of the cochlea provide spatial cues for localization and source separation. Spike features are generated with low latencies from the separated source spikes and are used by both KWS and SV which rely on state-of-the-art deep recurrent neural network architectures with a small memory footprint. Evaluation on a self-recorded event dataset based on TIDIGITS shows accuracies of over 93% and 88% on KWS and SV respectively, with minimum system latency of 5 ms on a limited resource device.
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