LRWR: Large-Scale Benchmark for Lip Reading in Russian language

Evgeniy Egorov; Mikhail Konyk; Sergey Kolesnikov; Vasily Kostyumov

LRWR: Large-Scale Benchmark for Lip Reading in Russian language

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arxiv 2109.06692 v1 pith:BRBR7XAI submitted 2021-09-14 cs.CV cs.LG

Evgeniy Egorov , Vasily Kostyumov , Mikhail Konyk , Sergey Kolesnikov This is my paper · ORCID

classification cs.CVcs.LG

keywords lipreadingbenchmarklrwrdatasetdetailedlanguagelanguageslarge-scale

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Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas. One of the significant obstacles for research in this field is the lack of proper datasets for a wide variety of languages: so far, these methods have been focused only on English or Chinese. In this paper, we introduce a naturally distributed large-scale benchmark for lipreading in Russian language, named LRWR, which contains 235 classes and 135 speakers. We provide a detailed description of the dataset collection pipeline and dataset statistics. We also present a comprehensive comparison of the current popular lipreading methods on LRWR and conduct a detailed analysis of their performance. The results demonstrate the differences between the benchmarked languages and provide several promising directions for lipreading models finetuning. Thanks to our findings, we also achieved new state-of-the-art results on the LRW benchmark.

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An end-to-end Spanish lipreading system based on the CTC/Attention architecture achieves state-of-the-art WER of 24.8% on VLRF and 34.5%/59.5% on LIP-RTVE speaker-dependent/independent partitions, with an ablation stu...

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