LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild
This provides a benchmark for researchers in lipreading, particularly for Mandarin applications, but it is incremental as it builds on existing dataset efforts.
The authors tackled the problem of visual speech recognition by introducing LRW-1000, a large-scale lipreading dataset with 1,000 classes and 718,018 samples, which is currently the largest word-level and only public large-scale Mandarin dataset, and they evaluated several methods to demonstrate its consistency and challenges.
Large-scale datasets have successively proven their fundamental importance in several research fields, especially for early progress in some emerging topics. In this paper, we focus on the problem of visual speech recognition, also known as lipreading, which has received increasing interest in recent years. We present a naturally-distributed large-scale benchmark for lip reading in the wild, named LRW-1000, which contains 1,000 classes with 718,018 samples from more than 2,000 individual speakers. Each class corresponds to the syllables of a Mandarin word composed of one or several Chinese characters. To the best of our knowledge, it is currently the largest word-level lipreading dataset and also the only public large-scale Mandarin lipreading dataset. This dataset aims at covering a "natural" variability over different speech modes and imaging conditions to incorporate challenges encountered in practical applications. It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up. Besides providing a detailed description of the dataset and its collection pipeline, we evaluate several typical popular lipreading methods and perform a thorough analysis of the results from several aspects. The results demonstrate the consistency and challenges of our dataset, which may open up some new promising directions for future work.