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Heri Nieto

dblp:245/6040 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 100%
Artificial intelligence
1 paper
Speech recognition and synthesis · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › input sensing › gesture recognition
accelerometer-based gesture recognition
0.412019
Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches · KDD 2019
Interaction techniques and input › mobile interaction › wearable device interaction
smartwatch interaction
0.412019
Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches · KDD 2019

Methods — techniques the papers use, named apart from their topics

policy model · 0.8false trigger mitigation · 0.8convolutional neural network · 0.8
YearPublicationVenuePosition
2019 Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches
abstract
The two most common ways to activate intelligent voice assistants (IVAs) are button presses and trigger phrases. This paper describes a new way to invoke IVAs on smartwatches: simply raise your hand and speak naturally. To achieve this experience, we designed an accurate, low-power detector that works on a wide range of environments and activity scenarios with minimal impact to battery life, memory footprint, and processor utilization. The raise to speak (RTS) detector consists of four main compo- nents: an on-device gesture convolutional neural network (CNN) that uses accelerometer data to detect specific poses; an on-device speech CNN to detect proximal human speech; a policy model to combine signals from the motion and speech detector; and an off-device false trigger mitigation (FTM) system to reduce unin- tentional invocations trigged by the on-device detector. Majority of the components of the detector run on-device to preserve user privacy. The RTS detector was released in watchOS 5.0 and is running on millions of devices worldwide.
Shiwen Zhao, Brandt Westing, Shawn Scully, Heri Nieto, Roman Holenstein, Minwoo Jeong, Krishna Sridhar, Brandon Newendorp, Mike Bastian, Sethu Raman, Tim Paek, Kevin Lynch, Carlos Guestrin
KDD4