In this paper, we consider the task of spotting spoken keywords in silent
video sequences -- also known as visual keyword spotting. To this end, we
investigate Transformer-based models that ingest two streams, a visual encoding
of the video and a phonetic encoding of the keyword, and output the temporal
location of the keyword if present. Our contributions are as follows: (1) We
propose a novel architecture, the Transpotter, that uses full cross-modal
attention between the visual and phonetic streams; (2) We show through
extensive evaluations that our model outperforms the prior state-of-the-art
visual keyword spotting and lip reading methods on the challenging LRW, LRS2,
LRS3 datasets by a large margin; (3) We demonstrate the ability of our model to
spot words under the extreme conditions of isolated mouthings in sign language
videos.

本研究提出 Transpotter 模型，使用全面的跨模态注意力机制在视觉和语音流之间进行交互，成功实现静默视频序列中的语音关键词检测，并且在多项测试中，优于当前视觉关键词检测和唇语识别模型，并具备较强的嘴型单词分离的能力。

注意力视觉关键词检测

Visual Keyword Spotting with Attention

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 state-of-the-art visual keyword spotting
architecture when trained and tested on the same benchmark, and also that of a
state-of-the-art lip reading method.

本研究提出一种名为 KWS-Net 的卷积神经网络结构，通过序列匹配和模式检测技术，从视觉角度自动检测并确定在何时，是否有包含关键词的口型出现，在无音频或有清晰 / 嘈杂语音的情况下，性能优于现有的同类方法，还在跨语言处理上实现了良好的表现。

观察唤醒词：视听关键词检测

Seeing wake words: Audio-visual Keyword Spotting

Visual keyword spotting (KWS) is the problem of estimating whether a text
query occurs in a given recording using only video information. This paper
focuses on visual KWS for words unseen during training, a real-world, practical
setting which so far has received no attention by the community. To this end,
we devise an end-to-end architecture comprising (a) a state-of-the-art visual
feature extractor based on spatiotemporal Residual Networks, (b) a
grapheme-to-phoneme model based on sequence-to-sequence neural networks, and
(c) a stack of recurrent neural networks which learn how to correlate visual
features with the keyword representation. Different to prior works on KWS,
which try to learn word representations merely from sequences of graphemes
(i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder
model which learns how to map words to their pronunciation. We demonstrate that
our system obtains very promising visual-only KWS results on the challenging
LRS2 database, for keywords unseen during training. We also show that our
system outperforms a baseline which addresses KWS via automatic speech
recognition (ASR), while it drastically improves over other recently proposed
ASR-free KWS methods.

本论文针对实际应用中未被训练过的词语进行视觉关键词检测的问题，并使用端到端的多层神经网络架构，使用语音图形编码器解决了此问题，该模型在 LRS2 数据集上取得了非常有前途的结果。