Utterance-level Feature Extraction in Text-independent Speaker Recognition: A Review
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摘要: 句级 (Utterance-level) 特征提取是文本无关说话人识别领域中的重要研究方向之一. 与只能刻画短时语音特性的帧级 (Frame-level) 特征相比, 句级特征中包含了更丰富的说话人个性信息; 且不同时长语音的句级特征均具有固定维度, 更便于与大多数常用的模式识别方法相结合. 近年来, 句级特征提取的研究取得了很大的进展, 鉴于其在说话人识别中的重要地位, 本文对近期具有代表性的句级特征提取方法与技术进行整理与综述, 并分别从前端处理、基于任务分段式与驱动式策略的特征提取方法, 以及后端处理等方面进行论述, 最后对未来的研究趋势展开探讨与分析.Abstract: Utterance-level feature extraction is one of the most important researches in text-independent speaker recognition. Compared with the frame-level features which only contain the short-term speech characteristics, the utterance-level features can effectively capture more speaker discriminative information. Meanwhile, it also has another advantage that any utterance with a variable duration can be represented as a fixed-dimension feature. Thus, the utterance-level features are easy to integrate with most commonly-used pattern recognition methods. In recent years, the researches on utterance-level feature extraction have made great progress. Considering the importance of utterance-level feature extraction in speaker recognition, this paper will organize and summarize the typical methods. Specifically, the front-end processing, the feature extraction based on the task-segmented strategy and task-driven strategy, and the back-end processing are introduced respectively. Finally, the future trends in speaker recognition are discussed and analyzed.
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图 3 帧级特征序列经特征规整后的直方图对比
Fig. 3 Histogram comparison of frame-level feature sequences after feature normalization
图 6 两种目标函数对应网络的结构示意图对比
Fig. 6 Comparison of the structure of the networks corresponding to the two different objective functions
表 1 不同特征空间学习方法汇总信息
Table 1 Information of different feature space learning methods
方法 描述 特点 经典MAP方法[29] $ {\boldsymbol{M}}_{s,h}={\boldsymbol{m}}+{\boldsymbol{D}}{\boldsymbol{z}}_{s,h} $ MAP 自适应方法 $ {\boldsymbol{D}} $为对角矩阵, $ {\boldsymbol{z}}_{s,h} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ 无法进行信道补偿 本征音模型[36-37] $ {\boldsymbol{M}}_{s,h}={\boldsymbol{m}}+{\boldsymbol{V}}{\boldsymbol{y}}_{s,h} $ 能够获得低维句级特征表示 $ {\boldsymbol{V}} $为低秩矩阵, $ {\boldsymbol{y}}_{s,h} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ 无法进行信道补偿 本征信道模型[37] $ {\boldsymbol{M}}_{s,h}={\boldsymbol{m}}+{\boldsymbol{D}}{\boldsymbol{z}}_{s}+{\boldsymbol{U}}{\boldsymbol{x}}_{h} $ 能够进行信道补偿 $ {\boldsymbol{D}} $为对角矩阵, $ {\boldsymbol{z}}_{s} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ 需要提供同一说话人的多信道语音数据 $ {\boldsymbol{U}} $为低秩矩阵, $ {\boldsymbol{y}}_{s,h} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ 说话人子空间中包含残差信息 联合因子分析模型[38] ${\boldsymbol{M} }_{s,h}={\boldsymbol{m} }+V{\boldsymbol{y} }_{s}+{\boldsymbol{U} }{\boldsymbol{x} }_{h}+{\boldsymbol{D} }{\boldsymbol{z} }_{s,h}$ 独立学习说话人信息与信道信息
需要提供同一说话人的多信道语音数据, 计算复杂度高$ {\boldsymbol{V}} $为低秩矩阵, $ {\boldsymbol{y}}_{s} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ $ {\boldsymbol{U}} $为低秩矩阵, $ {\boldsymbol{x}}_{h} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ $ {\boldsymbol{D}} $为对角矩阵, $ {\boldsymbol{z}}_{s} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ 总变化空间模型[39-40] $ {\boldsymbol{M}}_{s,h}={\boldsymbol{m}}+{\boldsymbol{T}}{\boldsymbol{w}}_{s,h}+{\boldsymbol{\varepsilon}}_{s,h} $ 学习均值超矢量中的全部变化信息 $ {\boldsymbol{T}} $为低秩矩阵, $ {\boldsymbol{w}}_{s,h} \sim {\rm{N}}\left({\bf{0}},{\boldsymbol{I}}\right) $ 获取 I-vector 特征后再进行会话补偿 $ {\boldsymbol{\varepsilon}}_{s,h} $为残差矢量 $ {\boldsymbol{\varepsilon}}_{s,h} $在不同方法中的形式不同 
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表 2 基于不同残差假设的无监督总变化空间模型
Table 2 Unsupervised TVS model based on different residual assumptions
方法 描述 E 步 M 步 计算复杂度 FEFA[40] $ {{\boldsymbol{M} }_{s,h}={\boldsymbol{m} }+{\boldsymbol{T} }{\boldsymbol{w} }_{s,h}}$
输入为统计量无残差假设${\begin{align}&{\boldsymbol{L} }={\left({\boldsymbol{I} }+\displaystyle\sum\limits_{c=1}^{C}{N}_{s,h}^{c}{ {\boldsymbol{T} } }_{c}^{\rm{T} }{\boldsymbol{\Sigma }}_{c}^{-1}{ {\boldsymbol{T} } }_{c}\right)}^{-1}\\ &{\boldsymbol{E} }={\boldsymbol{L} }\displaystyle\sum\limits_{c=1}^{C}{ {\boldsymbol{T} } }_{c}^{\rm{T} }{\boldsymbol{\Sigma } }_{c}^{-1}\left({\boldsymbol{F} }_{s,h}^{c}-{N}_{s,h}^{c}{\boldsymbol{\mu } }_{c}\right)\\ &\Upsilon ={\boldsymbol{L}}+{\boldsymbol{E}}{{\boldsymbol{E}}}^{\rm{T}}\end{align}} $ $ {{ {\boldsymbol{T} } }_{c}=\left[\displaystyle\sum\limits_{s,h}\left({\boldsymbol{F} }_{s,h}^{c}-{N}_{s,h}^{c}{\boldsymbol{\mu } }_{c}\right){\boldsymbol{E} }\right]{\left(\displaystyle\sum\limits_{s,h}{N}_{s,h}^{c}\Upsilon \right)}^{-1}}$ $ { {\rm{O}}\left(CFR+C{R}^{2}+{R}^{3}\right)} $ PPCA[43-44] $ { {\boldsymbol{M}}_{s,h}={\boldsymbol{m}}+{\boldsymbol{T}}{\boldsymbol{w}}_{s,h}+{\boldsymbol{\varepsilon}}_{s,h}} $
残差协方差矩阵各向同性$ {\begin{align}&{\boldsymbol{L} }={\left({\boldsymbol{I} }+\dfrac{1}{ {\sigma }^{2} }{ {\boldsymbol{T} } }^{\rm{T} }{\boldsymbol{T} }\right)}^{-1}\\ &{\boldsymbol{E} }=\dfrac{1}{ {\sigma }^{2} }{\boldsymbol{L} }{ {\boldsymbol{T} } }^{\rm{T} }\left({\boldsymbol{M} }_{s,h}-{\boldsymbol{m} }\right)\\ &\Upsilon ={\boldsymbol{L}}+{\boldsymbol{E}}{{\boldsymbol{E}}}^{\rm{T}} \end{align}}$ $ {\begin{aligned}{\boldsymbol{T} }=&\left[\displaystyle\sum\limits_{s,h}\left({\boldsymbol{M} }_{s,h}-{\boldsymbol{m} }\right){\boldsymbol{E} }\right]{\left(\displaystyle\sum\limits_{s,h}\Upsilon \right)}^{-1}\\{\sigma }^{2}=&\;\dfrac{1}{CF\displaystyle\sum\limits _{s,h}1}\{ {\left({\boldsymbol{M} }_{s,h}-{\boldsymbol{m} }\right)}^{\rm{T} }\left({\boldsymbol{M} }_{s,h}-{\boldsymbol{m} }\right)-\\ &{\rm{t} }{\rm{r} }\left(\Upsilon { {\boldsymbol{T} } }^{\rm{T} }{\boldsymbol{T} })\right\} \end{aligned} }$ $ {{\rm{O}}\left(CFR\right) }$ FA[44-45] $ { {\boldsymbol{M}}_{s,h}={\boldsymbol{m}}+{\boldsymbol{T}}{\boldsymbol{w}}_{s,h}+{\boldsymbol{\varepsilon}}_{s,h} }$
残差协方差矩阵各向异性$ {\begin{align} &{\boldsymbol{L}}={\left({\boldsymbol{I}}+{{\boldsymbol{T}}}^{\rm{T}}{\boldsymbol{\varPhi }}^{-1}{\boldsymbol{T}}\right)}^{-1}\\ &{\boldsymbol{E}}={\boldsymbol{L}}{{\boldsymbol{T}}}^{\rm{T}}{\boldsymbol{\varPhi }}^{-1}\left({\boldsymbol{M}}_{s,h}-{\boldsymbol{m}}\right) \\ &\Upsilon ={\boldsymbol{L}}+{\boldsymbol{E}}{{\boldsymbol{E}}}^{\rm{T}}\end{align}} $ $ {\begin{aligned}{\boldsymbol{T} }=&\left[\displaystyle\sum\limits_{ {\boldsymbol{s} },{\boldsymbol{h} } }\left({\boldsymbol{M} }_{ {\boldsymbol{s} },{\boldsymbol{h} } }-{\boldsymbol{m} }\right){\boldsymbol{E} }\right]{\left(\displaystyle\sum\limits_{s,h}\Upsilon \right)}^{-1}\\ {\sigma }^{2}=\;&\dfrac{1}{CF\displaystyle\sum\limits _{s,h}1}\{\left({\boldsymbol{M} }_{s,h}-{\boldsymbol{m} }\right){\left({\boldsymbol{M} }_{s,h}-{\boldsymbol{m} }\right)}^{\rm{T} }-\\ &{ {\boldsymbol{T} } }^{\rm{T} }\Upsilon {\boldsymbol{T} }\}\odot {\boldsymbol{I} } \end{aligned} }$ $ { {\rm{O}}\left(CFR\right)} $
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表 3 基于不同映射关系假设的无监督总变化空间模型
Table 3 Unsupervised TVS model based on different mapping relations
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表 6 不同目标函数汇总信息
Table 6 Information of different objective functions
目标 方法 目标函数 多分类 交叉熵 ${L_{{\rm{cro}}} } = - [y\log \hat y + (1 - y)\log (1 - \hat y)]$ Softmax ${L_s} = - \dfrac{1}{N}\displaystyle \sum\limits_{n = 1}^N {\log } \frac{ { { {\rm{e} } ^{ {\boldsymbol{\theta } }_{ {y_n} }^{\rm{T} }f({ {\boldsymbol{x} }_n})} } } }{ {\displaystyle \sum\limits_{k = 1}^K { { {\rm{e} } ^{ {\boldsymbol{\theta } }_k^{\rm{T} }f({ {\boldsymbol{x} }_n})} } } } }$ Center[98] ${L}_{c}=\dfrac{1}{2N}{\displaystyle \sum\limits_{n=1}^{N}\Vert f(}{\boldsymbol{x} }_{n})-{\boldsymbol{c} }_{ {y}_{n} }{\Vert }^{2}$ L-softmax[99] ${L}_{{\rm{l}}\text{-}{\rm{s}}}=-\dfrac{1}{N}{\displaystyle \sum\limits_{n=1}^{N}{\rm{log} } }\displaystyle\frac{ {\rm{e} }^{\Vert { {\boldsymbol{\theta } } }_{ {y}_{n} }\Vert \Vert f({\boldsymbol{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})} }{ {\rm{e} }^{\Vert { {\boldsymbol{\theta } } }_{ {y}_{n} }\Vert \Vert f({\boldsymbol{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})}+{\displaystyle \sum\limits_{k\ne {y}_{n} }{\rm{e} }^{\Vert { {\boldsymbol{\theta } } }_{k}\Vert \Vert f({\boldsymbol{x} }_{n})\Vert {\rm{cos} }({\alpha }_{k,n})} } }$ A-softmax[100] ${L}_{{\rm{a}}\text{-}{\rm{s}}}=-\dfrac{1}{N}{\displaystyle \sum\limits_{n=1}^{N}{\rm{log} } }\displaystyle\frac{ {\rm{e} }^{\Vert f({\boldsymbol{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})} }{ {\rm{e} }^{\Vert f({\boldsymbol{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})}+{\displaystyle \sum\limits_{k\ne {y}_{n} }{\rm{e} }^{\Vert { {\boldsymbol{\theta } } }_{k}\Vert \Vert f({\boldsymbol{x} }_{n})\Vert {\rm{cos} }({\alpha }_{k,n})} } }$ AM-softmax[101] ${L_{{\rm{am}}\text{-}{\rm{s}}} } = - \dfrac{1}{N}\displaystyle \sum\limits_{n = 1}^N {\log } \frac{ { { {\rm{e} } ^{s \cdot [\cos ({\alpha _{ {y_n},n} }) - m]} } } }{ { { {\rm{e} } ^{s \cdot [\cos ({\alpha _{ {y_n},n} }) - m]} } + \displaystyle \sum\limits_{k \ne {y_n} } { { {\rm{e} } ^{\cos ({\alpha _{k,n} })} } } } }$ 度量学习 Contrastive[102] ${L_{{\rm{con}}} } = yd\left[ {f({ {\boldsymbol{\boldsymbol{x} } }_1}),f({ {\boldsymbol{\boldsymbol{x} } }_2})} \right] + (1 - y)\max \{ 0,m - d\left[ {f({ {\boldsymbol{\boldsymbol{x} } }_1}),f({ {\boldsymbol{\boldsymbol{x} } }_2})} \right]\}$ Triplet[103] ${L_{{\rm{trip}}} } = \max \{ 0,d\left[ {f({ {\boldsymbol{\boldsymbol{x} } }_p}),f({ {\boldsymbol{\boldsymbol{x} } }_a})} \right] - d\left[ {f({ {\boldsymbol{\boldsymbol{x} } }_n}),f({ {\boldsymbol{\boldsymbol{x} } }_a})} \right] + m\}$
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表 8 常用数据库信息
Table 8 Information of common databases
数据库 年份 声学环境 类别数 语音段数/总时长 开源 CN-CELEB[126] 2019 多媒体 1000 300 h √ VoxCeleb[89]: VoxCeleb1[73] 2017 多媒体 1251 153516 √ VoxCeleb2[75] 2018 多媒体 6112 1128246 √ SITW[127] 2016 多媒体 299 2800 √ Forensic Comparison[128] 2015 电话 552 1264 √ NIST SRE12[129] 2012 电话/麦克风 2000+ — — ELSDSR[130] 2005 纯净语音 22 198 √ SWITCHBOARD[131] 1992 电话 3114 33039 — TIMIT[132] 1990 纯净语音 630 6300 —
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