Deep spoken keyword spotting: An overview
Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …
Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers
Executing machine learning workloads locally on resource constrained microcontrollers
(MCUs) promises to drastically expand the application space of IoT. However, so-called …
(MCUs) promises to drastically expand the application space of IoT. However, so-called …
Keyword transformer: A self-attention model for keyword spotting
The Transformer architecture has been successful across many domains, including natural
language processing, computer vision and speech recognition. In keyword spotting, self …
language processing, computer vision and speech recognition. In keyword spotting, self …
Broadcasted residual learning for efficient keyword spotting
Keyword spotting is an important research field because it plays a key role in device wake-
up and user interaction on smart devices. However, it is challenging to minimize errors while …
up and user interaction on smart devices. However, it is challenging to minimize errors while …
Streaming keyword spotting on mobile devices
O Rybakov, N Kononenko, N Subrahmanya… - arXiv preprint arXiv …, 2020 - arxiv.org
In this work we explore the latency and accuracy of keyword spotting (KWS) models in
streaming and non-streaming modes on mobile phones. NN model conversion from non …
streaming and non-streaming modes on mobile phones. NN model conversion from non …
Widening access to applied machine learning with tinyml
VJ Reddi, B Plancher, S Kennedy, L Moroney… - arXiv preprint arXiv …, 2021 - arxiv.org
Broadening access to both computational and educational resources is critical to diffusing
machine-learning (ML) innovation. However, today, most ML resources and experts are …
machine-learning (ML) innovation. However, today, most ML resources and experts are …
Matchboxnet: 1d time-channel separable convolutional neural network architecture for speech commands recognition
S Majumdar, B Ginsburg - arXiv preprint arXiv:2004.08531, 2020 - arxiv.org
We present an MatchboxNet-an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D time …
recognition. MatchboxNet is a deep residual network composed from blocks of 1D time …
Lightweight neural architecture search for temporal convolutional networks at the edge
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the
structure of Deep Learning (DL) models for complex tasks such as Image Classification or …
structure of Deep Learning (DL) models for complex tasks such as Image Classification or …
Small-footprint keyword spotting on raw audio data with sinc-convolutions
S Mittermaier, L Kürzinger… - ICASSP 2020-2020 …, 2020 - ieeexplore.ieee.org
Keyword Spotting (KWS) enables speech-based user interaction on smart devices. Always-
on and battery-powered application scenarios for smart devices put constraints on hardware …
on and battery-powered application scenarios for smart devices put constraints on hardware …
Wav2kws: Transfer learning from speech representations for keyword spotting
With the expanding development of on-device artificial intelligence, voice-enabled devices
such as smart speakers, wearables, and other on-device or edge processing systems have …
such as smart speakers, wearables, and other on-device or edge processing systems have …