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, 本文将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4方面进行论述, 最后还将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]$ {{M}}_{s,h}={{m}}+{{D}}{{z}}_{s,h} $ MAP自适应方法 $ {{D}} $为对角矩阵,
$ {{z}}_{s,h} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $无法进行信道补偿 本征音
模型[36, 37]$ {{M}}_{s,h}={{m}}+{{V}}{{y}}_{s,h} $ 能够获得低维句
级特征表示$ {{V}} $为低秩矩阵,
$ {{y}}_{s,h} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $无法进行信道补偿 本征信道
模型[37]$ {{M}}_{s,h}={{m}}+{{D}}{{z}}_{s}+{{U}}{{x}}_{h} $ 能够进行信道补偿 $ {{D}} $为对角矩阵,
$ {{z}}_{s} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $需要提供同一说话人的
多信道语音数据$ {{U}} $为低秩矩阵,
$ {{y}}_{s,h} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $说话人子空间中包
含残差信息联合因子分
析模型[38]${{M} }_{s,h}={{m} }+V{{y} }_{s}+{{U} }{{x} }_{h}+{{D} }{{z} }_{s,h}$ 独立学习说话人
信息与信道信息$ {{V}} $为低秩矩阵,
$ {{y}}_{s} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $需要提供同一说话人
的多信道语音数据
计算复杂度高$ {{U}} $为低秩矩阵,
$ {{x}}_{h} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $$ {{D}} $为对角矩阵,
$ {{z}}_{s} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $总变化空
间模型[39, 40]$ {{M}}_{s,h}={{m}}+{{T}}{{w}}_{s,h}+{{\varepsilon}}_{s,h} $ 学习均值超矢量中的
全部变化信息$ {{T}} $为低秩矩阵,
$ {{w}}_{s,h} \sim {\mathbb{N}}\left({\bf{0}},{{I}}\right) $获取i-vector特征后
再进行会话补偿$ {{\varepsilon}}_{s,h} $为残差矢量 $ {{\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] $ {{M}}_{s,h}={{m}}+{{T}}{{w}}_{s,h} $
输入为统计量无残差假设$\begin{align}&{{L} }={\left({{I} }+\displaystyle\sum\limits_{c=1}^{C}{N}_{s,h}^{c}{ {{T} } }_{c}^{\rm{T} }{\Sigma }_{c}^{-1}{ {{T} } }_{c}\right)}^{-1}\\ &{{E} }={{L} }\displaystyle\sum\limits_{c=1}^{C}{ {{T} } }_{c}^{\rm{T} }{{\varSigma } }_{c}^{-1}\left({{F} }_{s,h}^{c}-{N}_{s,h}^{c}{{\mu } }_{c}\right)\\ &\Upsilon ={{L}}+{{E}}{{{E}}}^{\rm{T}}\end{align} $ ${ {{T} } }_{c}=\!\left[\displaystyle\sum\limits_{s,h}\left({{F} }_{s,h}^{c}\!-\!{N}_{s,h}^{c}{{\mu } }_{c}\right){{E} }\right]\!\!{\left(\displaystyle\sum\limits_{s,h}{N}_{s,h}^{c}\Upsilon \right)}^{-1}$ $ {\rm{O}}\left(CFR+C{R}^{2}+{R}^{3}\right) $ PPCA[43, 44] $ {{M}}_{s,h}={{m}}+{{T}}{{w}}_{s,h}+{{\varepsilon}}_{s,h} $
残差协方差矩阵各向同性$\begin{align}&{{L} }={\left({{I} }+\dfrac{1}{ {\sigma }^{2} }{ {{T} } }^{\rm{T} }{{T} }\right)}^{-1}\\ &{{E} }=\dfrac{1}{ {\sigma }^{2} }{{L} }{ {{T} } }^{\rm{T} }\left({{M} }_{s,h}-{{m} }\right)\\ &\Upsilon ={{L}}+{{E}}{{{E}}}^{\rm{T}} \end{align}$ $\begin{align}&{{T} }=\left[\displaystyle\sum\limits_{s,h}\left({{M} }_{s,h}-{{m} }\right){{E} }\right]{\left(\displaystyle\sum\limits_{s,h}\Upsilon \right)}^{-1}\\& {\sigma }^{2}=\dfrac{1}{CF{\displaystyle\sum\limits }_{s,h}1}\{ {\left({{M} }_{s,h}-{{m} }\right)}^{\rm{T} }\left({{M} }_{s,h}-{{m} }\right)-\\ &{\rm{T} }{\rm{r} }\left(\Upsilon { {{T} } }^{\rm{T} }{{T} })\right\} \end{align}$ $ {\rm{O}}\left(CFR\right) $ FA[44, 45] $ {{M}}_{s,h}={{m}}+{{T}}{{w}}_{s,h}+{{\varepsilon}}_{s,h} $
残差协方差矩阵各向异性$ \begin{align} &{{L}}={\left({{I}}+{{{T}}}^{\rm{T}}{{\varPhi }}^{-1}{{T}}\right)}^{-1}\\ &{{E}}={{L}}{{{T}}}^{\rm{T}}{{\varPhi }}^{-1}\left({{M}}_{s,h}-{{m}}\right) \\ &\Upsilon ={{L}}+{{E}}{{{E}}}^{\rm{T}}\end{align} $ $\begin{align}&{{T} }=\left[\displaystyle\sum\limits_{ {{s} },{{h} } }\left({{M} }_{ {{s} },{{h} } }-{{m} }\right){{E} }\right]{\left(\displaystyle\sum\limits_{s,h}\Upsilon \right)}^{-1}\\ &{\sigma }^{2}=\dfrac{1}{CF{\displaystyle\sum\limits }_{s,h}1}\{\left({{M} }_{s,h}-{{m} }\right){\left({{M} }_{s,h}-{{m} }\right)}^{\rm{T} }-\\ &{ {{T} } }^{\rm{T} }\Upsilon {{T} }\}\odot {{I} } \end{align}$ $ {\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 ${L_{cro}} = - [y\log \hat y + (1 - y)\log (1 - \hat y)]$ Softmax ${L_s} = - \dfrac{1}{N}\displaystyle \sum\limits_{n = 1}^N {\log } \frac{ { { {\rm{e} } ^{ {{\theta } }_{ {y_n} }^{\rm{T} }f({ {{x} }_n})} } } }{ {\displaystyle \sum\limits_{k = 1}^K { { {\rm{e} } ^{ {{\theta } }_k^{\rm{T} }f({ {{x} }_n})} } } } }$ Center[98] ${L}_{c}=\dfrac{1}{2N}{\displaystyle \sum\limits_{n=1}^{N}\Vert f(}{{x} }_{n})-{{c} }_{ {y}_{n} }{\Vert }^{2}$ L-softmax[99] ${L}_{l-s}=-\dfrac{1}{N}{\displaystyle \sum\limits_{n=1}^{N}{\rm{log} } }\frac{ {\rm{e} }^{\Vert { {{\theta } } }_{ {y}_{n} }\Vert \Vert f({{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})} }{ {\rm{e} }^{\Vert { {{\theta } } }_{ {y}_{n} }\Vert \Vert f({{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})}+{\displaystyle \sum\limits_{k\ne {y}_{n} }{\rm{e} }^{\Vert { {{\theta } } }_{k}\Vert \Vert f({{x} }_{n})\Vert {\rm{cos} }({\alpha }_{k,n})} } }$ A-softmax[100] ${L}_{a-s}=-\dfrac{1}{N}{\displaystyle \sum\limits_{n=1}^{N}{\rm{log} } }\frac{ {\rm{e} }^{\Vert f({{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})} }{ {\rm{e} }^{\Vert f({{x} }_{n})\Vert {\rm{cos} }(m{\alpha }_{ {y}_{n},n})}+{\displaystyle \sum\limits_{k\ne {y}_{n} }{\rm{e} }^{\Vert { {{\theta } } }_{k}\Vert \Vert f({{x} }_{n})\Vert {\rm{cos} }({\alpha }_{k,n})} } }$ AM-softmax[101] ${L_{am{\rm{ - } }s} } = - \dfrac{1}{N}\displaystyle \sum\limits_{n = 1}^N {\log } \frac{ { { {\rm{e} } ^{s \cdot [\cos ({\alpha _{ {y_n},n} }) - m]} } } }{ { { {\rm{e} } ^{s \cdot [\cos ({\alpha _{ {y_n},n} }) - m]} } + \displaystyle \sum\limits_{k \ne {y_n} } { { {\rm{e} } ^{\cos ({\alpha _{k,n} })} } } } }$ 度量学习 Contrastive[102] ${L_{con}} = yd\left[ {f({{{{x}}}_1}),f({{{{x}}}_2})} \right] + (1 - y)\max \{ 0,m - d\left[ {f({{{{x}}}_1}),f({{{{x}}}_2})} \right]\} $ Triplet[103] ${L_{trip}} = \max \{ 0,d\left[ {f({{{{x}}}_p}),f({{{{x}}}_a})} \right] - d\left[ {f({{{{x}}}_n}),f({{{{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 153,516 √ VoxCeleb2[75] 2018 多媒体 6112 1,128,246 √ 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 33,039 - TIMIT[132] 1990 纯净语音 630 6300 —
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