The State of the Art and Prospects of Lip Reading
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摘要: 唇读, 也称视觉语言识别, 旨在通过说话者嘴唇运动的视觉信息, 解码出其所说文本内容. 唇读是计算机视觉和模式识别领域的一个重要问题, 在公共安防、医疗、国防军事和影视娱乐等领域有着广泛的应用价值. 近年来, 深度学习技术极大地推动了唇读研究进展. 本文首先阐述了唇读研究的内容和意义, 并深入剖析了唇读研究面临的难点与挑战; 然后介绍了目前唇读研究的现状与发展水平, 对近期主流唇读方法进行了梳理、归类和评述, 包括传统方法和近期的基于深度学习的方法; 最后, 探讨唇读研究潜在的问题和可能的研究方向. 以期引起大家对唇读问题的关注与兴趣, 并推动与此相关问题的研究进展.Abstract: Lip reading, also known as visual speech recognition, aims to infer the content of a speech through the motion of the speaker´s mouth. Lip reading is an important issue in the field of computer vision and pattern recognition. It has a wide range of applications in the fields of public security, medical, defense military and professional filming. In recent years, deep learning technology has greatly promoted the progress of lip reading research. Starting from the definition of lip reading problem, this paper first expounds the content and significance of lip reading research, and deeply analyzes the difficulties and challenges of lip reading research. Then, the recent achievements of lip reading research are introduced, and the current mainstream lip reading methods are combed, categorized and reviewed as well, including traditional methods and recent methods based on deep learning. Finally, the potential problems and possible research directions of lip reading research are discussed to arouse the attention and interest of this research, and promote the research progress of related issues.
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图 2 唇读难点示例. (a)第一行为单词place的实例, 第二行为单词please的实例, 唇形变化难以区分, 图片来自GRID数据集; (b)上下两行分别为单词wind在不同上下文环境下的不同读法/wind/与/waind/实例, 唇形变化差异较大; (c)上下两行分别为两位说话人说同一个单词after的实例, 唇形变化存在差异, 图片来自LRS3-TED数据集; (d)说话人在说话过程中头部姿态实时变化实例. 上述对比实例均采用相同的视频时长和采样间隔.
Fig. 2 Challenging examples of lip reading. (a) The upper line is an instance of the word place, the lower line is an instance of the word please; (b) The upper and lower lines are respectively different pronunciation of word wind in different contexts; (c) The upper and lower lines respectively tell the same word after, with big difference in lip motion; (d) An example of a real-time change in the head posture of the speaker during the speech. The above comparison examples all use the same video duration and sampling interval.
图 4 唇读研究过程中代表性方法. 传统特征提取方法: 主动形状模型ASM[51], 主动表观模型AAM[39], HiLDA[38], LBP-TOP[52], 局部判别图模型[40], 图嵌入方法[53], 随机森林流形对齐RFMA[41], 隐变量方法[54]; 深度学习方法: DBN/CNN+HMM混合模型[42-48], SyncNet[55], LipNet[49], WLAS[10], Transformer[50], LCANet[56], V2P[15].
Fig. 4 Representative methods in the process of lip reading research. Traditional feature extraction methods:ASM[51], AAM[39], HiLDA[38], LBP-TOP[52], LDG[40], Graph Embedding[53], RFMA[41], Hidden variable method[54]; Deep learning based methods: DBN/CNN+HMM hybrid model[42-48], SyncNet[55], LipNet[49], WLAS[10], Transformer[50], LCANet[56], V2P[15].
图 5 线性变换特征提取方法一般流程
Fig. 5 The workflow of linear transformation feature extraction method
表 1 传统时空特征提取算法优缺点总结
Table 1 A summary of advantages and disadvantages of traditional spatiotemporal feature extraction methods
时空特征提取方法 代表性方法 优势 不足 基于表观的 全局图像线性变换[38,57,60-63],
图嵌入与流形[40-41, 53-54,65],
LBP-TOP[52，66], HOG[67], 光流[29, 68]···1) 特征提取速度快;
2) 无需复杂的人工建模.1) 对唇部区域提取精度要求高;
2) 对环境变化、姿态变化、噪声敏感;
3) 不同讲话者之间泛化性能较差.基于形状的 轮廓描述[69-72],
AFs[73], 形状模型[74-75]···1) 具有良好的可解释性;
2) 不同讲话者之间泛化性能较好;
3) 能有效去除冗余信息.1) 会造成部分有用信息丢失;
2) 需要大量的人工标注;
3) 对于姿态变化非常敏感.形状表观融合的 形状+表观特征串联[76-77],
形状表观模型[39]···1) 特征表达能力较强;
2) 不同讲话者之间泛化性能较好.1) 模型复杂，运算量大;
2) 需要大量的人工标注.
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表 3 单词、短语和语句识别数据集, 其中(s)代表不同语句的数量. 下载地址为: MIRACL-VC[171], LRW[172], LRW-1000[173], GRID[174], OuluVS[175], VIDTIMIT[176], LILiR[177], MOBIO[178], TCD-TIMIT[179], LRS[180], VLRF[181]
Table 3 Word, phrase and sentence lip reading datasets and their download link: MIRACL-VC[171], LRW[172], LRW-1000[173], GRID[174], OuluVS[175], VIDTIMIT[176], LILiR[177], MOBIO[178], TCD-TIMIT[179], LRS[180], VLRF[181]
数据集 语种 识别任务 词汇量 话语数目 说话人数目 姿态 分辨率 谷歌引用 发布年份 IBMViaVoice 英语 语句 10 500 24 325 290 0 704 × 480, 30 fps 299 2000 VIDTIMIT 英语 语句 346 (s) 430 43 0 512 × 384, 25 fps 51 2002 AVICAR 英语 语句 1 317 10 000 100 −15 $\sim$ 15720 × 480, 30 fps 170 2004 AV-TIMIT 英语 语句 450 (s) 4 660 233 0 720 × 480, 30 fps 127 2004 GRID 英语 短语 51 34 000 34 0 720 × 576, 25 fps 700 2006 IV2 法语 语句 15 (s) 4 500 300 0,90 780 × 576, 25 fps 19 2008 UWB-07-ICAV 捷克语 语句 7 550 (s) 10 000 50 0 720 × 576, 50 fps 16 2008 OuluVS 英语 短语 10 (s) 1 000 20 0 720 × 576, 25 fps 211 2009 WAPUSK20 英语 短语 52 2 000 20 0 640 × 480, 32 fps 16 2010 LILiR 英语 语句 1 000 2 400 12 0, 30, 45, 60, 90 720 × 576, 25 fps 67 2010 BL 法语 语句 238 (s) 4 046 17 0, 90 720 × 576, 25 fps 12 2011 UNMC-VIER 英语 语句 11 (s) 4 551 123 0, 90 708 × 640, 25 fps 8 2011 MOBIO 英语 语句 30 186 152 0 640 × 480, 16 fps 175 2012 MIRACL-VC 英语 单词 10 1 500 15 0 640 × 480, 15 fps 22 2014 短语 10 (s) 1 500 Austalk 英语 单词 966 966 000 1 000 0 640 × 480 11 2014 语句 59 (s) 59 000 MODALITY 英语 单词 182 (s) 231 35 0 1 920 × 1 080, 100 fps 23 2015 RM-3000 英语 语句 1 000 3 000 1 0 360 × 640, 60 fps 7 2015 IBM AV-ASR 英语 语句 10 400 262 0 704 × 480, 30 fps 103 2015 TCD-TIMIT 英语 语句 5 954 (s) 6 913 62 0, 30 1920 × 1080, 30 fps 59 2015 OuluVS2 英语 短语 10 1 590 53 0, 30, 45, 60, 90 1920 × 1080, 30 fps 46 2015 语句 530 (s) 530 LRW 英语 单词 500 550 000 1 000+ 0 $\sim$ 30256 × 256, 25 fps 115 2016 HAVRUS 俄语 语句 1 530 (s) 4 000 20 0 640 × 480, 200 fps 13 2016 LRS2-BBC 英语 语句 62 769 144 482 1 000+ 0 $\sim$ 30160 × 160, 25 fps 172 2017 VLRF 西班牙语 语句 1 374 10 200a 24 0 1 280 × 720, 50 fps 6 2017 LRS3-TED 英语 语句 70 000 151 819 1 000+ −90 $\sim$ 90224 × 224, 25 fps 2 2018 LRW-1000 中文 单词 1 000 745 187 2 000+ −90 $\sim$ 901 920 × 1 080, 25 fps 0 2018 LSVSR 英语 语句 127 055 2 934 899 1 000+ −30 $\sim$ 30128 × 128, 23 ~ 30 fps 16 2018
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表 2 字母、数字识别数据集. 下载地址为: AVLetters[152], AVICAR[153], XM2VTS[154], BANCA[155], CUAVE[156], VALID[157], CENSREC-1-AV[158], Austalk[159], OuluVS2[160]
Table 2 Alphabet and digit lip reading datasets and their download link: AVLetters[152], AVICAR[153], XM2VTS[154], BANCA[155], CUAVE[156], VALID[157], CENSREC-1-AV[158], Austalk[159], OuluVS2[160]
数据集 语种 识别任务 类别数目 话语数目 说话人数目 姿态 分辨率 谷歌引用 发布年份 AVLetters 英语 字母 26 780 10 0 376 × 288, 25 fps 507 1998 XM2VTS 英语 数字 10 885 295 0 720 × 576, 25 fps 1 617 1999 BANCA 多语种 数字 10 29 952 208 0 720 × 576, 25 fps 530 2003 AVICAR 英语 字母 26 26 000 100 −15 $\sim$ 15720 × 480, 30 fps 170 2004 数字 13 23 000 CUAVE 英语 数字 10 7 000+ 36 −90, 0, 90 720 × 480, 30 fps 292 2002 VALID 英语 数字 10 530 106 0 720 × 576, 25 fps 38 2005 AVLetters2 英语 字母 26 910 5 0 1 920 × 1 080, 50 fps 62 2008 IBMSR 英语 数字 10 1 661 38 −90, 0, 90 368 × 240, 30 fps 17 2008 CENSREC-1-AV 日语 数字 10 5 197 93 0 720 × 480, 30 fps 25 2010 QuLips 英语 数字 10 3 600 2 −90 $\sim$ 90720 × 576, 25 fps 21 2010 Austalk 英语 数字 10 24 000 1 000 0 640 × 480 11 2014 OuluVS2 英语 数字 10 159 53 0 $\sim$ 901 920 × 1 080, 30 fps 46 2015
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表 4 不同数据集下代表性方法比较
Table 4 Comparison of representative methods under different datasets
数据集 识别任务 参考文献 模型 主要实验条件 识别率 前端特征提取 后端分类器 音频信号 讲话者依赖 外部语言模型 最小识别单元 AVLetters 字母 [41] RFMA × √ × 字母 69.60 % [48] RTMRBM SVM √ √ × 字母 66.00 % [42] ST-PCA Autoencoder × × × 字母 64.40 % [52] LBP-TOP SVM × √ × 字母 62.80 % × × 43.50 % [193] DBNF+DCT LSTM × √ × 字母 58.10 % CUAVE 数字 [102] AAM HMM √ × × 数字 83.00 % [91] HOG+MBH SVM × × × 数字 70.10 % √ × 90.00 % [194] DBNF DNN-HMM × × × 音素 64.90 % [60] DCT HMM √ × × 数字 60.40 % LRW 单词 [128] 3D-CNN+ResNet BiLSTM × × × 单词 83.00 % [131] 3D-CNN+ResNet BiGRU × × × 单词 82.00 % √ × 98.00 % [10] CNN LSTM+Attention × × × 单词 76.20 % [9] CNN × × × 单词 61.10 % GRID 短语 [56] 3D-CNN+highway BiGRU+Attention × √ × 字符 97.10 % [10] CNN LSTM+Attention × √ × 单词 97.00 % [134] Feed-forward LSTM × √ × 单词 84.70 % √ 95.90 % [49] 3D-CNN BiGRU × × × 字符 93.40 % [126] HOG SVM × √ × 单词 71.20 % LRS3-TED 语句 [151] 3D-CNN+ResNet Transformer+seq2seq × × √ 字符 41.10 % Transformer +CTC 33.70 % [15] 3DCNN BiLSTM+CTC × × √ 音素 44.90 %
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