Seeing wake words: Audio-visual Keyword Spotting
Liliane Momeni, Triantafyllos Afouras, Themos Stafylakis and, Samuel Albanie, Andrew Zisserman

TL;DR
This paper introduces KWS-Net, a novel zero-shot audio-visual keyword spotting architecture that improves detection accuracy in wild videos, generalizes across languages, and outperforms previous state-of-the-art methods.
Contribution
The paper presents a new convolutional architecture, KWS-Net, that enhances visual keyword spotting by using similarity maps and demonstrates cross-language generalization with minimal language-specific data.
Findings
KWS-Net outperforms previous visual keyword spotting methods.
Visual keyword spotting benefits from audio when available.
The method generalizes well to French and German with fine-tuning.
Abstract
The goal of this work is to automatically determine whether and when a word of interest is spoken by a talking face, with or without the audio. We propose a zero-shot method suitable for in the wild videos. Our key contributions are: (1) a novel convolutional architecture, KWS-Net, that uses a similarity map intermediate representation to separate the task into (i) sequence matching, and (ii) pattern detection, to decide whether the word is there and when; (2) we demonstrate that if audio is available, visual keyword spotting improves the performance both for a clean and noisy audio signal. Finally, (3) we show that our method generalises to other languages, specifically French and German, and achieves a comparable performance to English with less language specific data, by fine-tuning the network pre-trained on English. The method exceeds the performance of the previous…
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Taxonomy
TopicsSpeech and Audio Processing · Music and Audio Processing · Face recognition and analysis
