The Lip Reading in the Wild (LRW) dataset a large-scale audio-visual database that contains 500 different words from over 1,000 speakers. Each utterance has 29 frames, whose boundary is centered around the target word. The database is divided into training, validation and test sets. The training set contains at least 800 utterances for each class while the validation and test sets contain 50 utterances. Source: Towards Pose-invariant Lip-Reading Image Source: https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrw1.html
The Oxford-BBC Lip Reading Sentences 2 (LRS2) dataset is one of the largest publicly available datasets for lip reading sentences in-the-wild. The database consists of mainly news and talk shows from BBC programs. Each sentence is up to 100 characters in length. The training, validation and test sets are divided according to broadcast date. It is a challenging set since it contains thousands of speakers without speaker labels and large variation in head pose. The pre-training set contains 96,318 utterances, the training set contains 45,839 utterances, the validation set contains 1,082 utterances and the test set contains 1,242 utterances. Source: Audio-visual Recognition of Overlapped speech for the LRS2 dataset Image Source: https://www.robots.ox.ac.uk/~vgg/data/lip_reading/lrs2.html
LRS3-TED is a multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research. Source: LRS3-TED: a large-scale dataset for visual speech recognition
The QMUL underGround Re-IDentification (GRID) dataset contains 250 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views. All images are captured from 8 disjoint camera views installed in a busy underground station. The figures beside show a snapshot of each of the camera views of the station and sample images in the dataset. The dataset is challenging due to variations of pose, colours, lighting changes; as well as poor image quality caused by low spatial resolution. Source: https://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html
LRW-1000 has been renamed as CAS-VSR-W1k.* It is a naturally-distributed large-scale benchmark for word-level lipreading in the wild, including 1000 classes with about 718,018 video samples from more than 2000 individual speakers. There are more than 1,000,000 Chinese character instances in total. Each class corresponds to the syllables of a Mandarin word which is composed by one or several Chinese characters. This dataset aims to cover a natural variability over different speech modes and imaging conditions to incorporate challenges encountered in practical applications. Source: VIPL Image Source: https://arxiv.org/pdf/1810.06990v6.pdf
The German Lipreading dataset consists of 250,000 publicly available videos of the faces of speakers of the Hessian Parliament, which was processed for word-level lip reading using an automatic pipeline. The format is similar to that of the English language Lip Reading in the Wild (LRW) dataset, with each H264-compressed MPEG-4 video encoding one word of interest in a context of 1.16 seconds duration, which yields compatibility for studying transfer learning between both datasets. Choosing video material based on naturally spoken language in a natural environment ensures more robust results for real-world applications than artificially generated datasets with as little noise as possible. The 500 different spoken words ranging between 4-18 characters in length each have 500 instances and separate MPEG-4 audio- and text metadata-files, originating from 1018 parliamentary sessions. Additionally, the complete TextGrid files containing the segmentation information of those sessions are also included. The size of the uncompressed dataset is 15GB.
A new large-scale, in-thewild Mandarin dataset, CAS-VSR-S101 with 101.1 hours of data. The videos are sourced from broadcast news and conversational programs in Chinese, covering a highly diverse set of topics, speakers and filming conditions. The lengths of the utterances are naturally distributed between 0.01s and 10.57s, and image qualities and resolutions vary. News accounts for 82.4% of the programs. 70.4% of the utterances depict news anchors, hosts and correspondents, while 29.6% are those of interviewees and guests. In addition, at a ratio of approximately 1.5 : 1, male and female appearances are relatively balanced. It is divided into train, validation and test sets by TV channels to minimize speaker overlap, and at a ratio of roughly 8 : 1 : 1.5 in terms of duration. The validation and test sets are composed of programs broadcast on provincial TV channels. The dataset is available for academic use under a license.
MIRACL-VC1 is a lip-reading dataset including both depth and color images. It can be used for diverse research fields like visual speech recognition, face detection, and biometrics. Fifteen speakers (five men and ten women) positioned in the frustum of an MS Kinect sensor and utter ten times a set of ten words and ten phrases (see the table below). Each instance of the dataset consists of a synchronized sequence of color and depth images (both of 640x480 pixels). The MIRACL-VC1 dataset contains a total number of 3000 instances.