CN1125436C - 会话自然语言的命令边界鉴别器及鉴别方法 - Google Patents

会话自然语言的命令边界鉴别器及鉴别方法 Download PDF

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CN1125436C
CN1125436C CN99121518.4A CN99121518A CN1125436C CN 1125436 C CN1125436 C CN 1125436C CN 99121518 A CN99121518 A CN 99121518A CN 1125436 C CN1125436 C CN 1125436C
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CN1252592A (zh
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G·N·拉马斯沃迈
J·克雷迪恩斯特
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Nuance Communications Inc
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    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/22Procedures used during a speech recognition process, e.g. man-machine dialogue
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • G10L2015/088Word spotting
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/22Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/223Execution procedure of a spoken command

Abstract

本发明的在会话自然语言系统中自动鉴别命令边界的设备包括:变换输入信号为识别文本的语音识别器和与该语音识别器连接、接收该识别文本和确定在该识别文本中是否存在命令的边界鉴别器,该边界鉴别器在识别文本中存在命令时输出该命令。还公开了在会话自然语言系统中鉴别命令边界的方法。

Description

会话自然语言的命令边界鉴别器及鉴别方法
本发明涉及语音识别,尤其涉及鉴别自然会话语言的命令边界的设备和方法。
自然语言用户接口系统包括允许说话人通过说出命令向其输入命令的系统。但是,已有的会话自然语言用户接口系统要求用户利用某种形式的手动输入、例如在命令之间停顿或用鼠标点击显示器上的麦克风控制按钮来指明命令的结束、即命令的边界。这种要求造成用户接口使用起来相当不方便,并且会产生不希望有的延迟。
因此,需要能够自动鉴别会话自然语言用户接口中的命令边界的可训练系统。
根据本发明,自动鉴别会话自然语言系统中的命令边界的设备包括把输入信号变换为识别文本的语音识别器和与该语音识别器连接的边界鉴别器,该边界鉴别器接收识别文本,确定在该识别文本中是否存在命令,如果存在命令,就输出该命令。
在另一实施例中,边界鉴别器可向执行命令的应用程序输出。边界鉴别器可包括对识别文本进行处理的输入处理器。该输入处理器可以通过给识别文本中的每一个字添加该字关于假设的命令边界的相对位置来对该识别文本进行处理。边界鉴别器可进一步包括与该输入处理器连接的特征检测器,该特征检测器确定在已处理的识别文本中存在一组特征函数中的哪些特征函数。边界鉴别器可进一步包括根据对应于已处理的识别文本中的那些特征函数的一组特征权重判断在已处理的识别文本中是否存在命令的判定装置。该判定装置可以与特征检测器连接,可确定已处理的识别文本是否包含命令边界。
在再一实施例中,还可包括把设备训练成为能够识别文本和能够识别完整命令的训练系统。该训练系统可包括对由包含完整命令和非完整命令的话语组成的一组数据进行处理的输入处理器。该输入处理器可在训练数据中在每一话语前插入标记。该输入处理器可在识别文本中在第一个话语前、和在识别文本中在每一个命令后插入标记。可设置特征提取器来提取包含字和这些字关于于假设的命令边界位置的相对位置的特征函数。语音识别器可包括已利用训练数据进行了训练的语言模型,该训练数据包含被插入来表示训练数据中命令边界的位置的标记。语音识别器可包括标记的可加基本型。语音识别器可产生包含标记的识别文本。边界鉴别器可在识别文本中存在过长的寂静时断言存在命令边界。
机器可读的程序存储设备,真实体现可被机器执行来完成鉴别识别文本中的命令的方法步骤的指令程序,这些方法步骤包括:输入识别文本,通过给识别文本中的字添加关于假设的命令边界的相对位置来对该识别文本进行处理,根据一组特征函数确定在已处理的识别文本中的特征函数,利用所确定的特征函数判断已处理的识别文本是否包含命令,这种判断是根据特征函数的加权作出,以及如果包含命令就输出该命令。
在另一实施例中,可以包括通过输入由包含命令和非命令的话语组成的训练数据来训练程序存储设备的指令程序。可以包含在每一话语前设置标记的步骤。还可以包含在话语所包含的每一命令边界后设置标记的步骤。训练程序存储设备的指令程序可以包含从训练数据中提取特征函数的步骤。训练程序存储设备的指令程序可以包含为所有特征函数确定特征权重的步骤。处理识别文本的指令程序可以包含在识别文本中在第一个话语前和在识别文本中在每一个命令后设置标记的步骤。程序存储设备可进一步包括提供识别文本的语音识别器。
识别自然会话语言中的命令的方法包括以下步骤:输入识别文本,通过给识别文本中的字添加关于假设的命令边界的相对位置来对该识别文本进行处理,根据一组特征函数确定在已处理的识别文本中的特征函数,利用所确定的特征函数判断已处理的识别文本是否包含命令,这种判断是根据特征函数的加权作出,以及如果包含命令就输出该命令。
在另一方法中,可以包含输入包括由命令和非命令组成的话语的训练数据的步骤。还可以包含在训练数据的每一话语前设置标记的步骤。该方法可以进一步包含在话语所包含的命令边界后设置标记的步骤。该方法可以包含从训练数据中提取特征函数的步骤。该方法可进一步包含为所有特征函数确定特征权重的步骤。可以包含在识别文本中在第一个话语前和在识别文本中在每一个命令后设置标记的步骤。还可以包含把命令输出给执行该命令的设备的步骤,该设备包括提供识别文本的语音识别器。
本发明的这些以及其它目的、特点和优点在阅读了以下参看附图对其例示性实施例的详细描述之后将一目了然。
在以下参看附图的对最佳实施例的说明中详细描述本发明,其中:
图1是本发明的包括边界鉴别器的系统/方法的方框图/流程图;
图2是本发明的使用边界鉴别器产生的完整命令的应用的方框图/流程图;
图3是本发明的边界鉴别器的方框图/流程图;
图4是本发明的产生供边界鉴别器使用的特征函数和特征权重的设备的方框图/流程图;
图5是本发明的产生将供边界鉴别器使用的识别文本的语音识别器的方框图。
本发明涉及语音识别,尤其涉及鉴别自然会话语言的命令边界的设备和方法。本发明包括自动识别会话自然语言的命令字或短语的可训练系统。本发明提供了更加用户友好的接口,这种接口使用户能够更自然和连续地说话,不必手动指出命令边界。最好使用具有所有已标出的正确命令边界的最大熵识别模型。在训练期间,利用训练数据迭代选择一组特征及其权重。这些特征包括单词和短语,以及它们关于话语中的可能命令边界的相对位置。本发明的另一实施例包括用来为该识别模型产生有用标记的更有效的语言模型。
本发明提供了能够在会话自然语言用户接口中自动识别命令边界的设备。最好是,本发明可利用附加数据进行训练来改善性能,或可利用新域的数据进行训练以便可在该新域中使用该设备。本发明还可识别及分离单个话语中所包含的多个命令。本发明在识别期间使用极少的计算资源,使之可在实时系统中应用。
本发明使用来自自然语言理解和语音识别的统计技术。
最好通过利用最大熵识别模型首先给训练数据标记上命令边界。对于每一命令边界,给在窗口内的所有邻近字(包括在该边界左侧和右侧的字)加上标记,以指出它们关于边界的相对位置。然后对被进行了如此处理的训练数据执行最大熵特征提取,这些特征包括单词和短语以及它们关于边界的相对位置。利用迭代算法估算这些特征的相应权重。在译码期间,类似于标记当前串中每一个字关于命令边界的假设位置的相对位置处理测试句子。如果可能,还标记在边界的该假设位置之后出现的字。然后通过检查所出现的特征的权重的积确定是否把该假设位置认为是命令边界。
本发明还包括增强最大熵识别模型的方法。一种这样的增强包括在语音识别级使用更有效的语言模型。最好给语言模型训练数据中的命令边界加上标记,并使该模型包括边界的一组可加基本型(大多数基本型对应于各种形式的寂静)。由于这种添加,语音识别引擎将产生带有附加标记的一串文本来表明可能的命令边界。还描述了对识别模型的其它改进,例如利用延长的寂静时间的优点。
除识别命令边界外,本发明还可被用来识别同一句子中的多个命令。这就减轻了构成和支持复合命令的需要,这是由于利用相同的命令边界识别过程可自动分解多个命令的缘故。
应当懂得图1-5中所示的各部件可以用各种形式的硬件、软件或它们的组合来实现。最好在一台或多台恰当编程的、具有处理器、存储器和输入/输出接口的通用数字计算机上用软件来实现这些部件。现在参看在其中相同的标号表示相同或相似部件的各附图,首先参看图1,该图表示本发明的包括边界鉴别器的系统8的一个例子的流程图/方框图。音频输入10由系统8的用户产生,具有向系统8发出的口头命令的形式。例如,如果该系统是电子邮件应用,则用户发出的命令的一个例子可以是“检查新邮件”或“给我显示下面信息”。音频输入10被语音识别器20变换为识别文本30。语音识别器20的结构为本领域普通技术人员所了解。识别文本30是边界鉴别器40的输入,它产生完整命令50作为输出。如果识别文本30是完整命令,就把其作为输出进行传送。如果识别文本30不是完整命令,就没有输出被传送。对于该电子邮件应用,可以是一完整命令的识别文本的例子是“检查新邮件”和“给我显示下面信息”,不是完整命令的识别文本的例子是“检查新”、“给我显示”和“检查新邮件显示”。完整命令50被应用60使用。应用60最好是软件应用,完整命令50可用来打开该软件应用以及其它情况下与之有关的接口。本发明用途广泛,例如系统8可以与机械设备或电子设备对接。系统8能够把口头命令或音频信号变换为可执行信号,以便例如接通/关闭器具或调整设备/装置的特性或功能。
参看图2,该图表示使用完整命令的应用60的一个例子的方框图/流程图。应用60最好包括自然语言理解系统61和命令执行器62。自然语言系统61分析和解析完整命令50所包含的信息,产生正式命令,该正式命令被命令执行器61执行。例如,如果完整命令是“我有新信息吗?”,则自然语言理解系统可以把其变换为例如CheckNewMessage()这样的命令,并将该命令提供给命令执行器62。参看图3,该图表示边界鉴别器40一实例的方框图/流程图。边界鉴别器40把识别文本30作为输入,产生完整命令50作为输出。边界鉴别器40包括特征函数41、特征权重42、特征检测器43、输入处理器44和判定装置45。现在举例描述边界鉴别器40。在本发明的范围内可使用其它边界符号。识别文本30可用S来表示,边界鉴别器40判断S是否是一完整命令。如果S是一完整命令,就把用T表示的判定设定为T=1,否则设定为T=0。如果S是一完整命令,就把识别文本30作为边界识别器40的输出-它就是完整命令50-进行传送。因此边界鉴别器就负责了对两种T值估算条件概率P(T|S),选择使P(T|S)最小的T作为判定。
边界鉴别器40需要根据能够产生值P(T|S)的训练数据建立的模型。本发明最好通过利用最大熵原理产生值P(T|S),A.Berger等人发表在“计算语言学”(1996年3月,22卷,1期,39-71页)上的论文“自然语言处理的最大熵研究”对最大熵原理进行了描述,在此援引该论文作为参考。其它部件,例如特征检测器43、输入处理器44和判定装置45以及特征函数41和特征权重42将在下面详细进行描述。
参看图4,该图表示最大熵模型结构一实例的方框图/流程图。训练数据70包括包括大量与对应于完整命令的域相关的训练话语。根据这些话语,产生不对应于完整命令的一组话语,并同样将它们放入训练数据之中。对于这组扩大的训练数据的每一个条目,同样可确定正确的判定(T=0或T=1)。对于以上讨论的在话语“检查新邮件”后跟有“给我显示第一条消息”的电子邮件的例子,可在训练数据中作出以下条目。
检查//T=0
检查新//T=0
检查新邮件//T=1
检查新邮件显示//T=0
检查新邮件给我显示//T=0
在最后两个条目中,已加入了来自后面话语的字词,即“显示”和“给我显示”。有时需要这样的条目来解决可能产生的某些不定性。例如,象“删除”、“删除这”和“删除这一个”这样的话语都是完整的命令。在这些情况下,虽然“删除”本身可以是一完整命令,但当后面跟有“这”时就不是一完整命令了,同样,当后面跟有“这一个”时,“删除这”就不是一完整命令。因此,需要这种“向前看”步骤,向前看的字数-也称为向前看步骤的窗口尺寸-是本发明的参数之一。虽然根据应用的不同可设置其它窗口尺寸,但在本发明一实施例中,窗口尺寸是两个字。
输入处理器44处理训练数据70。对于命令边界的每一可能的位置,也称为命令边界的假设位置,如果训练集合中的一个字在假设命令边界左侧n个位置,输入处理器44就把给字添加-n,如果该字在假设命令边界右侧n个位置,输入处理器44就给该字添加+n。在输入处理器进行了处理之后,在被处理训练集合中的条目将变成:
检查-1//T=0
检查-2新-1//T=0
检查-3新-2邮件-1//T=1
检查-4新-3邮件-2显示-1//T=0
检查-5新-4邮件-3给我-2显示-1//T=0
检查-3新-2邮件-1显示+1//T=1
检查-3新-2邮件-1给我+1显示+2//T=1
检查-4新-3邮件-2给我-1显示+1//T=0
在以上例子中,加进了这些附加条目来适应上述向前看处理。
再参看图4,输入处理器44处理训练数据70,被处理的训练数据被特征提取器46用来产生特征函数41。在本发明一实施例中,可使用具有以下形式的特征函数:      (公式1)
其中i是特征的索引,i=1,...,n,特征的总数是n。特征函数包括被处理训练数据中的一个或多个字,还包括正确的判定。例如,考虑特征
                      f(新-2,邮件-1),(T=1)
如果在T=1的情况下,话语S包括分别在假设命令边界左侧第一和第二个位置处的字“新”和“邮件”,就使用这一特征。特征的总数n是本发明的一个参数,其值依赖于应用。每一个特征函数41包括带有被添加的相对位置的一个或多个字,还包括相应的判定(T=0或T=1)。从被输入处理器处理的训练数据中选择特征函数在已有技术中是公知的,可如Papineni等人在1997年在希腊Rhodes召开的EUROSPEECH大会上发表的论文“基于特征的语言理解”中所描述的那样进行选择,该论文援引于此作参考。
再参看图4,在特征提取器46产生了特征函数41之后,对于全部n个特征函数,特征权重计算器47计算特征权重42,包括特征函数ft,s i的权重αi。在本发明一实施例中,为例计算特征权重42,使用S.Della Pietra等人在1995年发表在卡内基梅隆大学计算机科学学院“技术报告CMU-CS95-144”上的论文“随机域的引发特征(inducing features of random fields)”中所描述的改进的迭代定标算法,该算法援引于此作为参考。联合分布P(T|S)的最大熵模型由以下公式2确定,该模型见A..Ratnaparkhi于1997年5月发表在宾西法尼亚大学识别科学研究所“报告97-08”上的论文“自然语言处理的最大熵模型简介”,该论文援引于此作为参考, P ( T , S ) = μ Π i = 1 n α i f t , s i ( T , S ) (公式2)
其中μ是规一化常数。
参看图3,对于识别文本30中的每一话语,输入处理器44给该话语中的每一字词添加关于给定的假设命令边界位置的相对位置,对于所有可能的命令边界位置重复这种添加。特征检测器43确定哪一个特征函数41在一给定的被处理话语中出现,判定装置45作该给定的被处理话语是否是一完整命令的最后判定。判定装置首先计算由公式3确定的P(T=1|S): P ( T = 1 | S ) = P ( T = 1 , S ) P ( T = 1 , S ) + P ( T = 0 , S ) (公式3)
当且仅当
        P(T=1|S)>P(T=0|S)           (公式4)
才认为话语S是一完整的命令。
本发明一实施例可用来改进本发明的性能。该实施例包括使用新标记来表示话语的开始。使用例如标记“SB”表示话语的开始,则被处理训练集合中的条目如下:
SB-4检查-3新-2邮件-1//T=1
SB-5检查-4新-3邮件-2显示-1//T=0
SB-4检查-3新-2邮件-1给我+1显示+2//T=1
SB-5检查-4新-3邮件-2给我-1显示+1//T=0
这给图3和图4中的输入处理器44的处理增加了一额外步骤。在图4中,训练数据70中的每一话语可包括位于每一话语开头的SB标记。在图3中,SB标记可插在第一个话语之前,对于后续话语,SB标记最好插在每一个被认可的命令边界之后。根据本发明,还可设想其它标记及其插入的位置。
参看图5,该图表示本发明另一实施例的方框图/流程图。语音识别器20包括一语言模型21和其它部件22。在本发明一实施例中,语言模型21是一增强模型,使语音识别器20能够产生还包含了表示命令边界的可能位置的新标记、例如SE的识别文本30。利用这种增强模型,语音识别器20产生包含例如“检查新邮件SE给我显示第一条信息SE...”这样的话语的识别文本30。为此,最好利用具有插在每一完整命令末尾处的SE标记的数据来建立语言模型21。可利用以上援引作为参考的由F.Jelinek描述的步骤来建立语言模型。为了支持该新的SE标记,给语音识别器20增加该标记的声学基本型。给该模型增加对应于各种形式寂静的该标记的声学基本型。在一实施例中,以下的声学基本型用于SE标记:
D$
X
XX
XXX
X AA X
X AO M X
X AO X
X AX X
X F X
X HH X
X K X
X P X
X TD X
再参看图4,训练数据70首先输入给语音识别器20,以产生SE标记,输入处理器44产生如下的被处理数据:
SB-5检查-4新-3邮件-2SE-1//T=1
SB-6检查-5新-4邮件-3SE-2显示-1//T=0
SB-5检查-4新-3邮件-2SE-1给我+1显示+2//T=1
SB-6检查-5新-4邮件-3给我-2SE-1显示+1//T=0
本发明另一实施例利用了话语中存在的任何持续的寂静阶段。根据该实施例,图3的判定装置45可在公式4规定的条件得到满足时、或在话语之间存在持续的寂静阶段时断言命令边界。在本发明一实施例中,如果存在例如3秒或3秒以上的寂静,判定装置45就断言命令边界。在本发明另一实施例中,用户可利用由接口向系统8提供的选择功能来选择所希望的寂静长度。
此处描述的鉴别命令边界的发明还可用来鉴别同一条话语中的多个命令的存在。命令边界可设置在对应一完整命令的每一部分话语的后面,由此把输入的话语分解成多个命令。例如,如果输入句子“检查新邮件给我显示第一个邮件”,则输出可以是:
SB-5检查-4新-3邮件-2SE-1//T=1
SB-7给我-6显示-5第一-4个-3邮件-2SE-1//T=1
已描述了会话自然语言的命令边界鉴别器的最佳实施例(这些最佳实施例用于说明而不是用于限制),注意本领域普通技术人员根据上述描述可作出各种改进和改动。因此应认识到可对在由所附权利要求书概括的本发明的范围和精髓之内的、在此所公开的本发明的具体实施例作出各种改动。如是已详细并特别根据专利法的要求描述了本发明,要求保护的内容在权利要求书中给出。

Claims (22)

1.一种会话自然语言系统中自动鉴别命令边界的设备,包括:
语音识别器,用于将输入信号变换为识别文本;
命令边界鉴别器,与该语音识别器连接,用于接收该识别文本,并确定在该识别文本中是否存在命令,如果在该识别文本中存在命令,则该边界鉴别器就输出该命令,其中该边界鉴别器包括一个输入处理器,通过给识别文本中的每一个字添加该字关于假设命令边界的相对位置来处理识别文本。
2.权利要求1的设备,其中的边界鉴别器向执行命令的应用输出。
3.权利要求1的设备,其中的边界鉴别器还包括与输入处理器连接的特征检测器,该特征检测器确定一组特征函数中的哪些特征函数在被处理的识别文本中出现。
4.权利要求3的设备,其中的边界鉴别器还包括判定装置,根据对应于在被处理的识别文本中的那些特征函数的一组特征权重,确定在该被处理的识别文本中是否存在命令。
5.权利要求4的设备,其中的判定装置与特征检测器连接,并确定被处理的识别文本中是否包括命令边界。
6.权利要求1的设备,还包括训练该设备识别文本和识别完整命令的训练系统。
7.权利要求6的设备,其中的训练系统包括对由一些话语组成的一组训练数据进行处理的输入处理器,这些话语包括完整命令和非完整命令。
8.权利要求7的设备,其中的输入处理器在训练数据的每一话语前插入标记。
9.权利要求7的设备,其中输入处理器在识别文本的第一个话语前、在识别文本的每一命令后插入标记。
10.权利要求6的设备,还包括提取特征函数的特征提取器,该特征函数包含单词和这些单词关于假设边界位置的相对位置。
11.权利要求1的设备,其中的语音识别器包括已被利用训练数据进行了训练的语言模型,该训练数据包括插入来表示在训练数据内的命令边界位置的标记。
12.权利要求11的设备,其中的语音识别器包括标记的附加基本型。
13.权利要求12的设备,其中的语音识别器产生包括标记的识别文本。
14.权利要求1的设备,其中的边界鉴别器在识别文本中存在持续的寂静阶段时断言命令边界。
15.一种鉴别自然会话语言中的命令的方法,包括以下步骤:
输入识别文本;
通过给识别文本中的单词添加相对于假设命令边界的位置来对该识别文本进行处理;
确定在被处理的识别文本中的、与一组特征函数一致的那些特征函数;
判断具有被确定的那些特征函数的被处理的识别文本是否包含命令,这种判断是根据特征函数的加权作出;以及
如果包含命令,则输出该命令。
16.权利要求15的方法,还包括向输入处理器输入包括由命令和非命令组成的话语的训练数据的步骤。
17.权利要求16的方法,还包含在话语包含的命令边界后设置标记的步骤。
18.权利要求16的方法,还包含从训练数据中提取特征函数的步骤。
19.权利要求16的方法,还包含为所有特征函数确定特征权重的步骤。
20.权利要求16的方法,还包含在识别文本的第一个话语前和在识别文本的每一个命令后设置标记的步骤。
21.权利要求15的方法,还包括在训练数据的每一话语前设置标记的步骤。
22.权利要求15的方法,还包含输出命令给执行该命令的设备的步骤,该设备包括提供识别文本的语音识别器。
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