CN101535983A - 协作会话语音用户界面的系统和方法 - Google Patents

协作会话语音用户界面的系统和方法 Download PDF

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CN101535983A
CN101535983A CNA2007800423150A CN200780042315A CN101535983A CN 101535983 A CN101535983 A CN 101535983A CN A2007800423150 A CNA2007800423150 A CN A2007800423150A CN 200780042315 A CN200780042315 A CN 200780042315A CN 101535983 A CN101535983 A CN 101535983A
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CN101535983B (zh
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L·贝尔德文
T·弗莱曼
M·特加尔弗
B·艾弗尔索德
C·威德尔
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Weber Assets Co., Ltd.
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    • G—PHYSICS
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    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/16—Sound input; Sound output
    • G06F3/167—Audio in a user interface, e.g. using voice commands for navigating, audio feedback
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1815—Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/1822—Parsing for meaning understanding
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G10L17/22—Interactive procedures; Man-machine interfaces
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/63—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for estimating an emotional state
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
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    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063—Training
    • G10L2015/0631—Creating reference templates; Clustering
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/225—Feedback of the input speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
    • G10L2015/228—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of application context
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    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
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    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming

Abstract

提供了协作会话语音用户界面。协作会话语音用户界面可以基于短期和长期共享知识，以生成有关用户发声的意图的一个或多个显式的和/或隐式的假设。可以基于变化的确定程度，对假设进行分级，并可以为用户生成适应性响应。可以基于确定程度，用言辞来表达响应，并使随后发声的适当域成帧。在一种实现方式中，错误识别可以容忍，可以基于随后的发声和/或响应，纠正会话过程。

Description

协作会话语音用户界面的系统和方法
技术领域
[001]本发明涉及人机语音用户界面的协作性会话模型。
背景技术
[002]技术的进步，特别是在收敛(convergence)空间内，导致了对语音识别软件的需求的增加，该软件可以以对人类直观的方式利用该技术。尽管由于共享信息和/或上下文以推进相互的谈话目标，人类之间的通信常常是“协作性的”，但是，现有的人机界面却不能提供相同级别的直观的交互。例如，谈话中的每一个人类参与者都可以为了受益于交换而对交换给出贡献。这是通过关于谈话的各个方面的共享的假设和预期完成的，如主题、参与者的有关主题的知识，对另一个参与者的有关主题的知识的预期，对于主题和/或参与者的适当的措词，基于以前的发声的谈话的发展，参与者的语气或语气变化，从每一个参与者预期的贡献的质量和数量，以及许多其他因素。参与连续地形成的并依赖共享的信息的谈话是人类交谈的天然而直观的方式。
[003]相比之下，复杂的人机界面不允许用户直观地利用技术，这阻碍了各种技术的大量的采用。利用语音界面，通过使交互更加容易，更快，有助于减轻此负担，但是，现有的语音界面(当他们实际工作时)仍需要用户进行很多的学习。即，现有的语音界面不能在古老的人机界面和谈话语音之间架起桥梁，使得与系统的交互感觉起来很普通。用户应该能够以普通的谈话方式直接从系统请求他们所需要的东西，无需记住准确的词语。或者，当用户不能确定特定需求时，他们应该能够使系统参加有成果的对话，以解决他们的请求。相反，现有的语音界面迫使用户为了配合简单语言的简单指令集以便以系统可以理解的方式发出请求而不说出他们的请求。通过使用现有的语音界面，用户和系统之间的对话几乎没有办法满足相互的目标。
[004]因此，现有的系统缺乏一种谈话语音模型，谈话语音模型可以给用户提供以对人类固有地直观的方式与系统进行交互的能力。现有的系统存在这些及其他问题。
发明内容
[005]根据本发明的各个实施例和方面，协作性的谈话语音用户界面可以理解自由形式的人类发声，把用户从被限制于固定的命令集和/或请求中解放出来。相反地，用户可以使用天然的、直观的，自由形式的表达方式，参与与机器的协作性的谈话，以完成请求或一系列请求。
[006]根据本发明的一个方面，提供了用于实现协作会话语音用户界面的示范性系统体系结构。系统可以接收输入，输入可以包括由输入设备接收到的人的发声，发声可以包括一个或多个请求。如这里所使用的，“发声”可以是字、音节、音素或由人发出的任何其他可听的声音。如这里所使用的，“请求”可以是设备、计算机或其他机器检索信息、执行任务或执行某种其他操作的命令、指令或其他指令。在一种实现方式中，输入可以是多模输入，多模输入的至少一部分是发声。输入的发声分量可以由自动语音识别器进行处理，以生成发声的一个或多个初步解释。然后，可以将一个或多个初步解释提供到会话语音引擎，以便进一步地进行处理，其中，会话语音引擎可以与一个或多个数据库进行通信，以生成自适应会话响应，响应可以作为输出返回到用户。在一种实现方式中，输出可以是多模输出。例如，发声可以包括执行操作的请求，而输出可以包括报告成功或失败的会话响应，以及操作的执行。
[007]根据本发明的另一个方面，示范性会话语音引擎可以生成对一个请求或一系列请求的自适应会话响应。会话语音引擎可以包括自由形式的语音搜索模块，该模块可以理解使用典型的日常的语言(即，自由形式的)的发声，并可以解释人通常如何说话的变化，它们使用的词汇量，以及他们说话时所处的条件。为解决人类语音的不可捉模的变化，自由形式的搜索模块可以包括偶然的人类语音的模型。例如，在一种实现方式中，自由形式的搜索模块可以理解特殊的行话和/或俚语，容忍词语顺序的变化，并容忍唠叨的暂停或口吃的语音。例如，动词在名词前面的形式化的英语请求可以以与名词在动词前面的请求等效的方式加以对待。在另一种实现方式中，在单一发声中可以标识带有多种变化的复合请求和/或复合任务。通过从单一发声中标识用于完成一个或多个任务的所有相关信息，可以提供优于诸如命令与控制系统之类的现有的语音用户界面(它们使用口头菜单将人可以提供的信息限制于给定点)的优点。在另一种实现方式中，通过从不完整的或模糊的请求推断预定的请求，可以提供谈话的感觉。通过模型化执行标识的上下文中的任务可能需要的上下文记号、限定符或其他信息，可以生成适应性响应，如提示用户提供丢失的上下文记号、限定符或其他信息。在一种实现方式中，响应可以以最限制可能的解释的方式要求遗漏信息，并可以使响应成帧，以建立随后的用户发声的域。在另一种实现方式中，可以识别名词和动词的常见的备选方案，以根据各种条件反映使用模式的变化。如此，表达中的变化可以得到支持，因为词序是不重要的或不曾预料到的，名词和/或动词可以以不同的方式表示，以给出简单化的，但典型的示例。在另一种实现方式中，可以从矛盾的或由于别的原因不准确的信息推断请求，如当发声包括开始和停止、重新开始、口吃、不停顿(run-on)的句子或其他有缺点的语音时。例如，用户可能有时改变主意，如此，在发声的过程中改变请求，尽管如此，有缺点的语音功能能够基于人语音的模型来推断请求。例如，各种模型可以指出最后一个条件最有可能正确、或语调、着重点、强调，词语“not”的使用等各种模型，或其他模型可以表示哪一个条件最有可能是正确的。
[008]根据本发明的另一个方面，会话语音引擎可以包括噪声耐量模块，该噪声耐量模块可以丢弃给定上下文中的没有含义的词或噪声，以降低混淆的可能性。此外，噪声耐量模块250还可以过滤掉环境和非人类的噪声，以进一步降低混淆的可能性。在一种实现方式中，噪声耐量模块可以与其他模块和功能协作，以过滤掉不适合标识的上下文的词。例如，噪声耐量模块可以过滤掉一系列一个或多个麦克风内的其他人类对话和/或发声。例如，单一设备可以包括多个麦克风，或者多个设备可以各自包括一个或多个麦克风，噪声耐量模块可以校对输入，并通过比较来自各个麦克风的语音信号来协作地过滤声音。噪声耐量模块也可以过滤掉多个麦克风内的非人类的环境噪音，由说话者的歧义或词语误用所引起的词汇表之外的词，或可能与目标请求不相关的其他噪声。噪声耐量模块的性能基准可以由基于人类的条件的噪声模型进行定义。例如，当汽车以65英里每小时行驶时窗户毁损，汽车的驾驶员有92％的可能被乘客所理解，那么，噪声耐量模块的性能基准在这样的条件下可以具有类似的性能。
[009]根据本发明的另一个方面，会话语音引擎可以包括上下文判断过程，用于确定一个请求的一个或多个上下文，以确定谈话内的含义。可以通过让一个或多个上下文域代理竞争来确定给定发声的最适当的域，确定一个或多个上下文。一旦一个给定的域“赢得”了竞争，获胜的域可以负责确定或推断进一步的上下文，并更新短期和长期共享知识。如果在上下文域代理之间存在僵持，则自适应会话响应可以提示用户消除僵持的代理之间的歧义。此外，上下文判断过程可以基于以前的发声和/或请求，推断预定的操作和/或上下文，而现有的系统独立地考虑每一个发声，潜在地一次又一次地产生相同的错误。例如，如果给定解释不正确，则可以从一个或多个自动语音识别器语法和/或从由会话语音引擎确定的可能的解释中删除不正确的解释作为潜在的解释，从而确保，对于完全相同的发声，同一个错误不会重复。
[010]上下文判断过程可以通过连续地更新现有上下文的一个或多个模型并确定不能先验地确定的上下文作为谈话的副产品，提供优于现有的语音用户界面的优点。相反，上下文判断过程可以跟踪谈话主题，并试图将当前发声匹配到最近的上下文中，包括在完成、部分地完成、请求了任务时在上下文之间进行切换。上下文判断过程可以通过定义在各种上下文域中可能对用户有用的相关的功能的集合，标识发声的一个或多个上下文域。此外，每一个上下文域都可以具有相关的词汇表和思想集合，以模型化词语分组，当一起评估时，可以消除一个上下文域与另一个上下文域的歧义。如此，当搜索相关组合时删去不符上下文的词语和噪声字可以增强推理的准确性。这提供了优于试图向发声的每个组件分配含义(即，包括不符上下文的词语和噪声字)的现有的系统的优点，导致几乎无限的可能的组合和混淆的可能性更大。上下文判断过程也可以是自我意识的，向一个或多个生成的假设分配确定程度，其中，假设可以发展，以反映环境条件的变化，说话者的歧义，口音或其他因素。通过标识上下文，上下文内的功能、上下文内的词汇表，从历史来看在上下文中最常常执行什么任务，什么任务刚刚完成等等，上下文判断过程可以根据相当弱的语音线索确定意图。此外，正如在人与人的谈话中，用户可以在任何时间切换上下文，而不会产生混淆，当发声明确时，可以快速地选择各种上下文域，而不出现菜单驱动的死路。
[011]根据本发明的另一个方面，示范性协作性会话模型可以基于自由形式的语音搜索、噪声耐量，以及上下文判断，以实现会话人机界面，该会话人机界面反映了人的交互和普通会话行为。即，协作性会话模型允许人和机器参与带有接受的目的或方向的谈话中，每一个参与者都对从中受益的谈话有贡献。通过利用有关人所依赖的发声的人的假定，无论是作为说话者和听者，人机界面可以类似于日常的人与人的谈话。在一种实现方式中，示范性协作性谈话模型可以获取传入的数据(共享知识)，以通知决定(智能假设构建)，然后，可以精炼判断，并生成响应(适应性响应构建)。
[012]根据本发明的另一个方面，共享知识可以包括短期和长期知识。短期知识可以在单一谈话过程中累积，其中，在单一谈话过程中接收到的输入可以保留。共享知识可以包括跨模态意识，其中，除累积涉及用户发声、请求、位置等等的输入之外，共享知识可以累积涉及其他模态输入的当前用户界面状态，以进一步构建共享知识模型。共享知识可以用来使用当前和相关信息，构建一个或多个智能假设，通过标识带有长期意义的信息，构建长期共享知识，并利用相关状态和措词信息，生成自适应响应。此外，由于协作性谈话模型化人类谈话，在心理上的适当时间量之后，短期会话数据会过期，从而人性化系统行为，降低基于过时的数据的上下文混淆的可能性，而同时还将来自过期的会话上下文的相关信息添加到长期知识模型中。长期共享知识一般可以是以用户为中心，而不是基于会话的，其中，可以随着时间的推移累积输入，以构建用户、环境、认识的、历史或其他长期知识模型。在用户参与协作性谈话过程中随时可以同时使用长期和短期共享知识。长期共享知识可以包括显式的和/或隐式的用户偏好，最近的上下文、请求、任务等等的历史，与词汇表相关的用户特定的行话和/或上下文的功能，最常使用的词语选择，或其他信息。长期共享知识可以用来使用当前和相关信息，构建一个或多个智能假设，当通过短期共享知识不可用时，利用适当的词语选择，生成自适应响应，精炼长期共享知识模型，标识特定任务的频率，标识用户经常感到困难的任务，或提供其他信息和/或分析，以生成更准确的会话响应。共享知识也可以用来修改未经提示的支持的级别(例如，对于新手与有经验的用户，经常被错认的用户等等)。如此，共享知识可以允许用户和语音用户界面共享假设和预期，如主题知识、谈话历史、词语使用、行话、语气或有助于人用户和系统之间的协作性谈话的其他假设和/或预期。
[013]根据本发明的另一个方面，对于任何给定发声，可以标识谈话类型。通过分类和发展各种交换的概念模型，可以不断地对齐用户预期和域功能。通过考虑谈话目标、参与者角色，和/或信息在参与者之间的分配，可以生成关于谈话类型的一个或多个智能假设。基于谈话目标，参与者角色，以及信息的分配，智能假设可以考虑各种因素，将谈话(或发声)分类为可以彼此进行交互的一般的谈话类型，以形成很多变化和谈话类型的置换(例如，随着从一个参与者向另一个参与者重新分配信息，或者随着谈话目标基于信息的重新分配而改变，谈话类型可以动态地变化)。
[014]根据本发明的另一个方面，智能假设可以包括发声中的用户的意图的一个或多个假设。此外，智能假设可以使用短期和/或长期共享知识，以主动地随着谈话的进行或随着时间的推移来构建和评估与用户的交互。这些假设可以模型化人与人的互动，以包括每一个假设的变化的确定程度。即，正如人依赖于参与者共享的知识来审查有多少信息以及有什么样的信息可用，智能假设可以利用标识的谈话类型和共享的知识来生成每一个假设的确定程度。
[015]根据本发明的另一个方面，可以从智能假设生成句法上、语法上以及在上下文中敏感的“智能响应”，这些响应可以用来生成用户的谈话体验，而同时还引导用户以有利于识别方式应答。智能响应可以通过适应用户的说话方式、适当地使响应成帧，具有天然变化和/或个性(例如，通过改变语气、速度、定时、语气变化，措词，行话及口头或音频响应中的其他变化)，产生谈话的感觉。
[016]根据本发明的另一个方面，智能响应可以通过使用上下文记号和语法规则，适应用户的说话方式，以生成可以与用户协作的一个或多个句子。通过利用有关用户如何发出请求的声音的共享知识，可以使用用于识别请求的类似技术，模型化响应。智能响应可以根据统计对可能的响应进行分级和/或使响应随机化，产生构建与天然变化和谈话的感觉交换的机会。这相对于输入和输出不一致的的现有的语音用户界面而言，具有优点，在现有的语音用户界面中，输入是“谈话的”，而输出是“电脑语言”。
[017]根据本发明的另一个方面，智能响应可以使响应成帧，以影响用户应答发声，以便于识别。例如，可以将响应模型化为更加可能导致完整的请求的发自用户的不当(illicit)的发声。如此，响应可以符合人的对话的协作性特征和天然的人的“模仿”刚刚听到的作为下一个发声的一部分的倾向。此外，当前上下文的知识可以增强响应，以生成更有意义的谈话响应。使响应成帧也可以根据人的模型，处理错误识别。例如，人经常记住许多最近的发声，特别是当一个或多个以前的发声被错误识别或无法识别时。谈话中的另一个参与者可能限制对错误识别的或无法识别的发声的一部分的纠正，或在随后的发声和/或其他交互中，可以提供线索以指出初始的解释不正确。如此，通过存储和分析多个发声，随着谈话的进行，可以纠正前面的谈话的发声。
[018]根据本发明的另一个方面，智能响应可以包括对用户的多模或跨模的响应。在一种实现方式中，响应可以识别并控制一个或多个设备和/或接口，用户可以通过使用最方便的任何一种输入方法或输入方法的组合来作出响应。
[019]根据本发明的另一个方面，智能响应可以纠正谈话的过程，而不会中断会话流。即，尽管智能响应可以合理地“确定”，但是，智能响应有时也可能不正确。尽管现有的语音用户界面趋于不出现通常的会话失误(misstep)，正常的人之间的交互则可能预期出现失误并相应地进行处理。如此，错误识别之后的响应可以在澄清而不是错误之后模型化，在随后的响应中可以选择词语，以使谈话继续进行，并确定与用户一起探查的适当的域。
[020]基于下面的图形和具体实施方式，本发明的其他目标和优点对所属领域的技术人员是显而易见的。
附图说明
[021]图1是根据本发明的一个方面的系统体系结构的示范性方框图。
[022]图2是根据本发明的一个方面的会话语音引擎的示范性方框图。
[023]图3是根据本发明的一个方面的协作性会话模型的示范性方框图。
具体实施方式
[024]请参看图1，该图显示了根据本发明的一个方面的用于实现协作会话语音用户界面的示范性系统体系结构。系统可以从用户那里接收输入，其中，在一种实现方式中，输入105可以包括由输入设备(例如，麦克风)接收到的人的发声，发声可以包括一个或多个请求。输入105也可以是多模输入，其中，多模输入的至少一部分是发声。例如，输入设备可以包括麦克风和触摸屏设备的组合，而输入105可以包括发声，该发声包括涉及用户正在触摸的触摸屏设备上的显示器的一部分的请求。例如，触摸屏设备可以是导航设备，输入105可以包括发声“Give me directions to here”，其中，用户可以在导航设备的显示器上请求到所需目的地的方向。
[025]输入105的发声分量可以由自动语音识别器110进行处理，以生成发声的一个或多个初步解释。自动语音识别器110可以使用当前技术已知的任何适用技术对发声进行处理。例如，在一种实现方式中，自动语音识别器110可以使用语音听写的技术，解释发声，以识别音素流，如标题为“Dynamic Speech Sharpening”的待审批的美国专利申请系列编号11/513,269所描述的，在此引用该申请的全部内容作为参考。然后，可以将由自动语音识别器110生成的一个或多个初步解释提供到会话语音引擎115，以便进一步进行处理。会话语音引擎115可以包括会话语言处理器120和/或语音搜索引擎125，如下面的图2比较详细地描述的。会话语音引擎115可以与一个或多个数据库130进行通信，以生成自适应会话响应，响应可以作为输出140返回到用户。在一种实现方式中，输出140可以是多模输出和/或与一个或多个应用程序145的交互，以完成请求。例如，输出140可以包括音频响应和导航设备上的路线的显示的组合。例如，发声可以包括执行操作的请求，而输出140可以包括报告成功或失败的会话响应，以及操作的执行。此外，在各种实现方式中，自动语音识别器110、会话语音引擎115，和/或数据库130可以驻留在本地(例如，在用户设备上)、远程(例如，在服务器上)，或者，也可以使用本地和远程处理的混合模式(例如，可以在本地对轻量级应用程序进行处理，而在远程对计算密集的应用程序进行处理)。
[026]请参看图2，该示范性方框图显示了根据本发明的一个方面的会话语音引擎215。会话语音引擎215可以包括会话语言处理器220，该处理器使用自由形式的语音搜索模块245、噪声耐量模块250，和/或上下文判断进程255，生成对一个请求或一系列请求的自适应会话响应。根据本发明的一个方面，模块245-255可以与语音搜索引擎225进行通信，语音搜索引擎225包括一个或多个上下文域代理230和/或一个或多个词汇表235，以帮助解释发声和生成响应，如Tom Freeman和Larry Baldwin所著的“Enhancing the VUETM(Voce-User-Experience)Through Conversational Speech”所描述的，在此引用该申请的全部内容作为参考。会话语音引擎215可以生成针对一个或多个请求的自适应会话响应，其中，请求可以取决于未说出口的假设、不完整的信息、由以前的发声建立的上下文、用户概况、历史概况、环境概况，或其他信息。此外，会话语音引擎215可以跟踪哪些请求已经完成，哪些请求正在被处理中，和/或哪些请求由于信息不完整或不准确而不能处理，并可以相应地生成响应。
[027]根据本发明的一个方面，自由形式的语音搜索模块245可以理解使用典型的日常的语言(即，自由形式的)的发声，并可以解释人通常如何说话的变化，它们使用的词汇表，以及他们说话时所处的条件。由于诸如应力、分心，以及偶然发现之类的可变因素始终是不同的并且无限地变化的，自由形式的搜索模块245可以这样设计，目标是应该理解，没有人以同样的方式来到相同的人机界面的情况两次。如此，自由形式的搜索模块245可以实现模型化偶然的人的语音的一个或多个特点。在各种实现方式中，自由形式的搜索模块245可以包括，其中，自由形式的发声功能、一步访问功能、推理计划的操作功能、交替表达功能，和/或有缺点的语音功能。
[028]自由形式的发声功能可以理解特殊的行话和/或俚语，容忍词语顺序的变化(例如，无论请求的主体先于或后于动词，可能不相关)，并容忍唠叨的暂停(例如，“um”、“ah”、“eh”，及其他没有意义的发声)。例如，自由形式的发声功能可以以与自由形式的请求(名词可能在动词前面)的等效的方式对待形式化的英语“动词在名词之前”的请求。例如，可以等效地对待用户的发声“Change it to theSquizz”和“You know，um，that Squizz channel，ah，switch it there”(其中，Squizz是XM Satellite Radio上的一个频道)。不论是哪一种情况，自由形式的发声功能能够将“Squizz”识别为发声的主体，而将“Change it”或“switch it”识别为发声的动词或请求(例如，通过与上下文判断过程255或者其他功能协作，并标识相关上下文域代理230和/或词汇表235，以解释发声)。
[029]一步访问功能可以理解包括带有多个变量的复合请求的发声。例如，用户发声可以是“What is the forecast for Boston thisweekend？”一步访问功能可以将“weather”识别为上下文(例如，通过与上下文判断过程255或其他功能协作，并将“forecast”识别为“weather”的同义词)，并搜索等于“Boston”的城市和等于“‘weekend”的时间。通过从单一发声中标识用于完成任务的所有相关信息，一步访问功能可以克服诸如命令与控制系统之类的现有的语音用户界面(它们使用口头菜单将人可以提供的信息限制于给定点，例如，电话目录服务的命令与控制系统可以说：“State please”，“City please”，“What listing”，等等)。此外，某些发声可以包括复合请求，一步访问功能可以将复合请求分解为多个子任务。例如，用户发声“I need tobe at a meeting tomorrow in San Francisco at 8:00 am”可以分解成一组子任务，如：(1)检查是否有会议之前的晚上的航班并预定一个航班，(2)检查是否有宾馆，并预定宾馆，(3)检查是否有小汽车并预定一辆汽车等等，其中，用户还可以进一步为各种任务指定偏好(例如，首先检查用户经常乘坐的航空公司是否有航班)。取决于有关用户的偏好和/或历史模式的共享知识的级别，一步访问功能可以根据请求推断另外的任务。例如，在上面的示例中，一步访问功能也可以检查天气预报，如果天气是“好”(如用户偏好所定义和/或根据历史模式推断)，一步访问功能可以安排旧金山的首选的高尔夫球场的开球时间。
[030]推理预定的操作功能可以从不完整的模糊的请求识别预定的请求。例如，当用户发出“Route<indecipherable>Chicago<indecipherable>here”，其中，用户打算说“Route calculation toChicago from here”，推理预定的操作功能可以模型化计算路线(起始点和目的地点)所需的东西。由于发声包括起始点和目的地点，可以推断计算从用户的当前位置到芝加哥的路线的请求。类似地，当推理预定的操作功能没有足够的信息来推断完整的请求时，可以生成自适应谈话响应以提示用户提供遗漏信息。例如，当发声包括股票行情的请求但没有公司名称(例如，“Get me the stock price for<indecipherable>”)时，响应可以是“What company′s stock quote doyou want？”然后，用户可以提供包括公司名称的发声，请求可以是完整的。在一种实现方式中，响应可以以最限制可能的解释的方式要求遗漏信息(例如，在需要城市和州的任务的请求中，可以首先要求州，因为州比城市的数量少)。此外，推理预定的操作功能可以通过在复合的任务和子任务级别维护上下文和识别相关和/或遗漏信息，模型化复合任务和/或请求。
[031]交替表达功能可以识别名词和动词的常见的备选方案，以根据各种条件反映使用模式的变化。例如，用户可以基于年龄、社会经济背景、种族、用户的怪念头或其他因素，改变表达方式。如此，交替表达功能可以支持表达方式的变化，词序是不重要的或不曾预料到的。基于各种条件或人口资料的表达式的备选方案可以加载到上下文域代理230和/或词汇表235中，交替表达功能可以基于推断的或新发现的变化，更新上下文域代理230和/或词汇表235。在一种实现方式中，谈话语音引擎215可以包括预订接口，以更新对上下文域代理230和/或词汇表235的更改(例如，储存库可以综合各种用户发声和部署全系统的更新)。在操作中，交替表达功能可以允许名词和/或动词以不同的方式表示，以给出简单化的，但典型的示例。例如，对Washington，D.C.的天气预报感兴趣的用户可能提供任何一个下面的发声，其中每一个都被等效地解释：“What′s the weather likein DC”，“Is it raining inside the Beltway”，“Gimme the forecast forthe capital”等等。类似地，“Go to my home”，“Go home”，“Show routeto home”和“I would like to know my way home”这些发声都可以等效地解释，其中，用户概况可以包括用户的家庭地址，可以计算到家庭地址的导航路线。
[032]有缺点的语音功能能够从矛盾的或由于别的原因不准确的信息推断请求，如当发声包括开始和停止、重新开始、口吃、run-on句子或其他有缺点的语音时。例如，用户可能有时改变主意，如此，在发声的过程中改变请求，尽管如此，有缺点的语音功能能够基于人语音的模型来推断请求。例如，对于发声“Well，I wanna...Mexi...no，steak restaurant please，I′m hungry”，现有的语音用户界面不会对关于人的语音的模型作出假设，不能推断用户是需要墨西哥餐厅还是牛排餐厅。有缺点的语音功能通过使用可能表示最后一个条件最有可能正确、或语调、着重点、强调，词语“not”的使用等人的理解的各种模型，或可能表示哪一个条件最有可能是正确的其他模型，来克服这些缺点。如此，在上面的示例中，有缺点的语音功能可以推断，用户需要牛排餐厅。
[033]根据本发明的一个方面，噪声耐量模块250可以与有缺点的语音功能紧密关联，并可以操作以丢弃给定上下文中的没有含义的词或噪声，以便不会产生混淆。此外，噪声耐量模块250还可以过滤掉环境和非人类的噪声，以进一步降低混淆的可能性。在一种实现方式中，噪声耐量模块250可以与其他模块和功能协作，以过滤掉不适合上下文的词。例如，可以标识一个或多个上下文，可以过滤掉相对于系统功能没有含义的词，没有含义的人类的随机的发声及其他噪声。如此，噪声耐量模块250可以模型化现实世界的条件，以标识有意义的请求。例如，噪声耐量模块250还可以过滤掉一系列一个或多个麦克风内的其他人类对话和/或发声。例如，单一设备可以包括多个麦克风，或者多个设备可以各自包括一个或多个麦克风，噪声耐量模块可以校对输入，并通过比较来自各个麦克风的语音信号来协作地过滤声音。噪声耐量模块250也可以过滤掉多个麦克风内的非人类的环境噪音，词汇表之外的词(可能是说话者的歧义或词语误用所造成的)，或可能与目标请求不相关的其他噪声。噪声耐量模块250中的噪声模型可以基于人类的条件，定义性能基准。例如，当汽车以65英里每小时行驶时窗户毁损，汽车的驾驶员有92％的可能被乘客所理解，那么，噪声耐量模块250在那些条件下可以具有类似的性能。
[034]根据本发明的一个方面，谈话语音引擎215可以包括上下文判断过程255，该过程确定一个请求的一个或多个上下文，以确定谈话内的含义。可以通过让一个或多个上下文域代理竞争来确定给定发声的最适当的域，确定一个或多个上下文，如标题为“Systems andMethods for Responding to Natural Language Speech Utterance”的编号为No.11/197,504的待审批的美国专利申请和标题为“MobileSystems and Methods of Supporting Natural LanguageHuman-Machine Interactions”的编号为No.11/212,693的待审批的美国专利申请所描述的，这里引用了这两个申请作为参考。一旦一个给定的上下文域代理“赢得”了竞争，获胜的代理可以负责确定或推断进一步的上下文，并更新短期和长期共享知识。如果在上下文域代理之间存在僵持，则自适应会话响应可以提示用户消除僵持的代理之间的歧义。例如，用户的发声“What about traffic？”可能在各种上下文中具有不同的含义。即，当用户正在查询系统的媒体播放器时具有第一种含义(即，“traffic”将是歌手/作曲家Steve Winwood领导的摇滚乐队)当用户正在查询关于Michael Douglas电影的搜索界面时具有第二种含义(即，“traffic”将是Steven Soderbergh导演的电影)，当用户正在查询导航设备以了解到机场的方向时具有第三种含义(即，“traffic”将与到机场的路上的道路的状况相关)。
[035]此外，上下文判断过程255可以基于以前的发声和/或请求，推断预定的操作和/或上下文，而现有的系统独立地考虑每一个发声，潜在地一次又一次地产生相同的错误。例如，如果给定解释不正确，则可以从一个或多个自动语音识别器语法和/或从由上下文判断过程255确定的可能的随后的解释中删除不正确的解释作为潜在的解释，从而确保，对于完全相同的发声，同一个错误不会重复。
[036]上下文判断过程255可以通过连续地更新现有上下文的一个或多个模型，来克服现有的系统的缺点，其中，确定上下文的过程可以是谈话的副产品，这是不能先验地确定的。上下文判断过程255可以随着任务完成、部分地完成，被请求，等等，确定第一上下文域、变为第二上下文域，变回到第一上下文域，上下文堆栈可以跟踪谈话主题，并试图将当前发声匹配到最近的上下文中，次最近的主题等等，遍历上下文堆栈，直到可以确定最有可能的意图。例如，用户可能发出“What′s the traffic report”，上下文判断过程255可以确定Traffic为上下文，并返回包括交通状况报告的输出，不正巧提及Interstate-5的交通状况。然后，用户可以发出“What about1-5？”，上下文判断过程255可以知道，当前上下文是交通，可以搜索包括有关Interstate-5的信息的交通报告，可以作为输出返回表明Interstate-5拥挤的交通状况报告。然后，用户可以“Is there a fasterway？”，上下文确定模块255可以知道，当前上下文仍是交通，并可以搜索交通不太拥挤的到指定的目的地的并避开Interstate-5的路线。此外，上下文判断过程255可以基于用户概况、环境概况、历史概况或其他信息，构建上下文，以进一步精炼上下文。例如，概况可以表明，Interstate-5是星期一到星期五行驶的典型的路线。
[037]当试图消除上下文之间的歧义时，概况特别有意义，词语在不同上下文中具有不同含义。例如，用户可以发出“What′s theweather in Seattle？”，上下文判断过程255可以确定天气为上下文，并确定西雅图作为环境上下文。然后，用户可以发出“and Portland？”，上下文判断过程255可以基于俄勒冈州波特兰市和华盛顿州西雅图市之间的天气和环境相似性，返回俄勒冈州波特兰市的天气报告。然后，用户可以询问“What time does the game start？”，并可能搜索来自西雅图和/或波特兰的运动队的体育赛事，根据下面的图3中比较详细地描述的方法，以会话方式呈现结果。相关地，如果在第二次发声中用户最初发出了“What′s the weather in Portsmouth，NewHampshire”’则上下文判断过程255可以基于与New Hampshire的环境相似性，检索缅因州波特兰市的天气报告。此外，当环境概况、上下文共享知识，和/或其他短期和/或长期共享知识不能提供足够的信息消除各种可能性之间的歧义时，响应可以提示用户提供更进一步的信息(例如，“Did you mean Portland，Maine，or Portland，Oregon？”)。
[038]上下文判断过程255可以与上下文域代理230协作，其中，每一个上下文域代理230都可以定义可能对用户有用的相关的功能的集合。此外，每一个上下文域代理230都可以包括相关的词汇表235和思想集合，以模型化词语分组，当一起评估时，可以消除一个上下文域与另一个上下文域的歧义(例如，音乐上下文域代理230可以包括歌曲、音乐家、专辑等等的词汇表235，而股票上下文域代理230可以包括公司名称、股票行情报价机符号，财务度量等等的词汇表235)。如此，当搜索相关组合时删去不符上下文的词语和噪声字可以增强识别含义的准确性。相比之下，现有的系统试图向发声的每个组件分配含义(例如，包括不符上下文的词语和噪声字)，导致几乎无限的可能的组合和混淆的可能性更大。此外，上下文域代理230可以包括进一步有助于解释发声、推断意图，完成不完整的请求等等的每一个条件的元数据(例如，Space Needle词汇表单词可以包括西雅图、界标、旅游、Sky City餐厅等等的元数据)。给定消除歧义的条件，上下文判断过程255如此能够自动地确定其他完成请求所需的信息，忽略词序的重要性，并执行谈话语音的其他增强。
[039]上下文域代理230也可以是自我意识的，向一个或多个生成的假设分配确定程度，其中，假设可以发展，以反映环境条件的变化，说话者的歧义，口音，或其他因素。在概念上，上下文域代理230可以被设计为模型化发声，因为耳背的人将在有噪声的一方。通过标识上下文，上下文内的功能、上下文内的词汇表，从历史来看在上下文中最常常执行什么任务，什么任务刚刚完成等等，上下文域代理230可以根据相当弱的语音线索确定意图。此外，上下文堆栈可以是用于确定上下文的多个组件中的一个，如此，不对用户构成约束。所有上下文域都是可访问的，允许用户在任何时间切换上下文，不会产生混淆。如此，正如在人与人的谈话中，当发声明确时，可以快速地选择上下文域，没有菜单驱动的死路。例如，用户可以发出“Pleasecall Rich Kennewick on his cell phone”，可以生成“Do you wish me tocall Rich Kennewick on his cell？”的系统响应。用户可以此时决定稍后呼叫Rich Kennewick，而相反，听一些音乐。如此，用户可以发出“No，play the Louis Armstrong version of Body and Soul from my iPod”，可以在通过媒体播放器播放Body and Soul时生成“Playing Bodyand Soul by Louis Armstrong”的系统响应。在此示例中，稍后的发声与第一次的发声没有上下文的联系，因为发声中的请求条件是明确的，可以轻松地切换上下文，而不依赖于上下文堆栈。
[040]请参看图3，该图显示了根据本发明的一个方面的示范性协作性会话模型300。协作性会话模型300可以基于自由形式的语音搜索245、噪声耐量250，以及上下文判断255，以实现会话人机界面，该会话人机界面反映了人如何彼此进行交互以及谈话中的他们的普通行为。简而言之，协作性会话模型300允许人和机器参与带有接受的目的或方向的谈话中，每一个参与者都对谈话有贡献。即，协作性谈话模型300包括了利用有关人依赖的发声的人的假定(无论是作为说话者和听者)的技术和进程流，从而创建了类似于日常的人与人的谈话的人机界面。在一种实现方式中，协作性谈话可以获取传入的数据(共享知识)305，以通知决定(智能假设构建)310，然后，可以精炼判断，并生成响应(适应性响应构建)315。
[041]根据本发明的一个方面，共享的知识305包括有关传入的数据的短期和长期知识。短期知识可以在单一谈话过程中累积，而长期知识可以随着时间的推移累积，以构建用户概况、环境概况、历史概况或认识概况等等。
[042]可以在会话输入累加器中保留在单一谈话过程中接收到的输入。会话输入累加器可以包括跨模态意识，其中，除累积涉及用户发声、请求、位置等等的输入之外，会话输入累加器还可以累积涉及其他模态输入的当前用户界面状态，以进一步构建共享知识模型，和更准确的自适应响应(例如，如上文所描述的，当用户发出涉及触摸屏设备的一部分的请求时)。例如，会话输入累加器可以累积输入，包括每一个发声的识别文本，每一个发声的记录的语音文件，列表项目选择历史、图形用户界面处理历史，或其他输入数据。如此，会话输入累加器可以利用当前和相关信息填充智能假设生成器310，通过标识带有长期意义的信息，构建长期共享知识，给适应性响应生成器315提供相关状态和词语使用信息，保留最近的上下文，以便与智能假设生成器310一起使用，和/或保留发声，以便在多遍评估过程中重新进行处理。此外，因为协作性谈话300模型化人类谈话，在心理上的适当时间量之后，短期会话数据会过期，从而人性化系统行为。例如，人大不可能回忆起两年之前的谈话的上下文，但是由于上下文可以通过机器识别，在预定的时间量之后会话上下文过期，降低基于过时的数据的上下文混淆的可能性。然而，可以向用户、历史、环境、认识的或其他长期知识模型中添加来自过期会话上下文的相关信息。
[043]长期共享知识一般可以是以用户为中心的，而不是基于会话的。即，可以随着时间的推移累积输入，以构建用户、环境、认识的、历史或其他长期知识模型。在用户参与协作性谈话300过程中随时可以同时使用长期和短期共享知识(统称为共享知识305)。长期共享知识可以包括显式的和/或隐式的用户偏好，最近使用的代理、上下文、请求、任务等等的历史，与词汇表相关的用户特定的行话和/或代理和/或上下文的功能，最常使用的词语选择，或其他信息。长期共享知识可以用来使用当前和相关信息，填充智能假设生成器310，当适当的词语选择通过会话输入累加器不可用时，给适应性响应生成器315提供适当的词语选择，精炼长期共享知识模型，标识特定任务的频率，标识用户经常感到困难的任务，或提供其他信息和/或分析，以生成更准确的会话响应。
[044]如上文所描述的，共享知识305可以用来填充智能假设生成器310，以便用户和语音用户界面可以共享假设和预期，如主题知识、谈话历史、词语使用、行话、语气(例如，正式的、幽默的，简洁的等等)，或有助于在人机界面中进行交互的其他假设和/或预期。
[045]根据本发明的一个方面，成功的协作性谈话的一个组件可以是根据发声识别谈话的类型。通过分类和发展各种交换的概念模型，可以不断地对齐用户预期和域功能。通过考虑谈话目标、参与者角色，和/或信息在参与者之间的分配，智能假设生成器310可以生成关于谈话类型的假设。谈话目标可以广泛地包括：(1)获取单独的信息片段或执行单独的任务，(2)收集相关的信息片段，以作出决定，和/或(3)传播或收集大量的信息，以构建专业知识。参与者角色可以广泛地包括：(1)控制谈话的主导，(2)跟随主导并应请求提供输入的支持者，和/或(3)使用信息的消费者。在谈话开始时，信息可以由一个或多个参与者持有，其中，参与者可以持有大多数(或所有)信息，少量(或没有)信息，或者信息可以在多个参与者之间大致平均地分配。基于谈话目标，参与者角色，以及信息的分配，智能假设生成器310可以考虑各种因素，将谈话(或发声)分类为可以彼此进行交互的一般的谈话类型，以形成很多变化和谈话类型的置换(例如，随着从一个参与者向另一个参与者重新分配信息，或者随着谈话目标基于信息的重新分配而改变，谈话类型可以动态地变化)。
[046]例如，在一种实现方式中，查询谈话可以包括获取单独的信息片段或执行特定的任务的谈话目标，其中，查询谈话的主导头脑中可能有特定目标，并可能引导谈话往下进行，以实现目标。其他参与者可能持有信息，并可能通过提供信息来支持主导。在启发式谈话中，谈话的主导可以控制谈话的支持者所需的信息。支持者的角色可以仅限于调节谈话的整个进展，并插入查询，以便澄清。在探讨性的谈话中，两个参与者都共享主导和支持者角色，谈话可能没有特定目标，或目标可以随着谈话的进行而即席实现。基于此模型，智能假设生成器310可以根据下面的图形广泛地对谈话(或发声)进行分类：
Figure A200780042315D00271
Figure A200780042315D00272
[047]智能假设生成器310可以使用标识的谈话类型，以有助于生成一组针对发声中的用户的意图的假设。此外，智能假设生成器310可以使用来自会话输入累加器的短期共享知识，以主动地随着谈话的进行来构建和评估与用户的交互，并使用长期共享知识，以主动地随着时间的推移来构建和评估与用户的交互。如此，智能假设生成器310可以自适应地取得有关用户意图的一组n个最佳假设，可以向适应性响应生成器315提供n个最佳假设。此外，智能假设生成器310可以通过计算每一个假设的确定程度来模型化人与人的互动。即，正如人依赖于参与者共享的知识来审查有多少信息以及有什么样的信息可用，智能假设生成器310可以利用标识的谈话类型和短期和长期共享知识来生成每一个假设的确定程度。
[048]根据本发明的另一个方面，当发声包含完成请求或任务所需的所有信息(包括限定符)时，智能假设生成器310可以生成用户的意图的一个或多个显式的假设。每一个假设都可以具有对应的确定程度，可以用来确定响应中提供的未经提示的支持的级别。例如，响应可以包括一个确认，以确保发声不会被误解，或者，响应可以自适应地提示用户提供遗漏的信息。
[049]根据本发明的另一个方面，当发声包含可能是丢失的必需的限定符或完成请求或任务所需的其他信息时，智能假设生成器310可以使用短期知识来生成用户的意图的一个或多个隐式假设。每一个假设都可以具有对应的确定程度。例如，当谈话开始时，存储在会话输入累加器中的短期知识可以是空的，随着谈话的进行，会话输入累加器可以构建谈话的历史。智能假设生成器310可以使用会话输入累加器中的数据来补充或推断有关当前发声的额外的信息。例如，智能假设生成器310可以基于与当前发声相关的许多以前的请求，来评估确定程度。在另一个示例中，当当前发声包含的信息不足以完成请求或任务时，会话输入累加器中的数据可以用来推断遗漏信息，以便可以生成假设。在再一个示例中，智能假设生成器310可以标识供适应性响应生成器315形成个性化的谈话的响应要使用的句法和/或语法。在再一个示例中，当当前发声包含完成请求或任务所需的阈值信息量时，可以依赖会话输入累加器中的数据来调整确定程度。
[050]根据本发明的另一个方面，当发声缺少完成请求或任务所需的限定符或其他信息时，智能假设生成器310可以使用长期共享知识来生成用户的意图的一个或多个隐式假设。每一个假设都可以具有对应的确定程度。使用长期知识的过程可以基本上类似于使用短期共享知识的过程，只是信息可以不受当前会话的约束，而输入机制可以包括来自不同于当前谈话内容的另外的来源的信息。例如，智能假设生成器310可以在任何时间使用来自长期共享知识的信息，甚至在启动新的谈话的情况下，而短期共享知识仅限于现有的谈话(当启动新的谈话时，没有短期共享知识可用)。长期共享知识可以来自多个来源，包括用户偏好或插入的数据源(例如，到远程数据库的预订接口)，用户的专业知识(例如，基于错误的频率，请求的任务类型，等等，可以将用户标识为新手、中等，有经验，或其他类型的用户)，也可以适用于其他代理的代理特定的信息和/或语言(例如，通过将信息与某一个代理分离，以将该信息归入其他代理)，来自会话输入累加器的经常使用的主题，经常使用的动词、名词或其他词类，和/或来自会话输入累加器的其他句法信息，或者也可以使用其他长期共享知识的来源。
[051]根据本发明的另一个方面，由智能假设生成器310生成的启用知识的发声，可以包括一个或多个显式的(由用户提供)，和一个或多个隐式(由智能假设生成器310提供)上下文记号、限定符、条件，及其他信息，它们可以用来标识和评估相关任务。此时，智能假设生成器310可以向适应性响应生成器315提供输入。由适应性响应生成器315接收到的输入可以至少包括假设的排序的列表，包括显式的和/或隐式的假设，每一个假设都可以具有对应的确定程度。可以给假设分配四个确定程度中的一个：(1)“确信”，其中，上下文记号和限定符涉及一个任务，上下文和限定符涉及一个任务，而ASR置信水平超过预定的阈值；(2)“十分确信”，其中，上下文记号和限定符涉及一个以上的任务(选择等级最高的任务)，条件涉及一个请求，和/或ASR置信水平低于预定的阈值；(3)“不确信”，其中，需要另外的上下文记号或限定符来表示任务或对任务进行分级；以及(4)“无假设”，其中，很少或没有信息可以解密。每一个确定程度都还可以进一步被分类为显式的或隐式的，可以用来调整响应。由适应性响应生成器310接收到的输入也可以包括上下文、用户句法和/或语法，上下文域代理特定的信息和/或偏好(例如，旅行上下文域代理可以知道，用户经常请求有关法国的信息，该信息可以与电影上下文域代理共享，以便响应有时候可以包括法语电影)。
[052]根据本发明的另一个方面，适应性响应生成器315可以构建句法上、语法上以及在上下文中敏感的“智能响应”，这些响应可以与一个或多个代理一起使用，生成用户的谈话体验，而同时还引导用户以有利于识别方式应答。在一种实现方式中，智能响应可以包括通过输出设备(例如，说话者)播放的口头或音频应答，和/或通过设备、计算机或机器执行的操作(例如，下载Web页面，显示列表，执行应用程序等等)。在一种实现方式中，适当的响应可能不需要谈话适应，可以使用默认应答和/或针对给定任务随机地选择的响应集合。
[053]根据本发明的另一个方面，适应性响应生成器310可以凭借由智能假设生成器310维护的信息，生成可以对上下文、当前发声的任务识别，用户知道有关主题的情况，应用程序已经知道有关主题的情况，关于用户偏好和/或相关标题的共享知识，适当的上下文措词(例如，行话)，用户在最近的发声中发出的词语，谈话的发展和/或过程校正，谈话的语气，谈话的类型，响应的措词的天然变化，或其他信息敏感的响应。结果，适应性响应生成器315可以生成产生谈话的感觉的智能响应，适应在谈话期间累积的信息，维护跨模意识，保持谈话过程。
[054]根据本发明的另一个方面，适应性响应生成器315可以通过适应用户的说话方式、适当地使响应成帧，具有天然变化和/或个性(例如，通过改变语气、速度、定时、语气变化，措词，行话及口头或音频响应中的其他变化)，产生谈话的感觉。适应用户的说话方式的过程可以包括使用上下文记号和语法规则来生成一个或多个句子，用作可以与用户协作的响应集合。通过利用有关用户如何发出请求的短期(来自会话输入累加器)和长期(来自一个或多个配置文件)共享知识，可以使用用于识别请求的技术，模型化响应。适应性响应生成器315可以根据统计对可能的响应进行分级和/或使响应随机化，产生构建与天然变化和谈话的感觉交换的机会。这相对于输入和输出不一致的的现有的语音用户界面而言，可能是显著的优点，在现有的语音用户界面中，输入是“谈话的”，而输出是“电脑语言”。下面的示例可以演示响应如何适应用户的输入词语选择和说话方式：
 
用户 Do you know[mumbled words]Seattle[moremumbled words]？                   
语音用户界面 Did you want Seattle sports scores，weather，traffic，or news？                      
 
用户 Find me[mumbled words]Seattle[more mumbledwords]？                            
语音用户界面 I found Seattle，did you want sports scores，weather，traffic，or news？            
 
用户 Get me[mumbled words]Seattle[moremumbled words]？               
语音用户界面 I′ve got Seattle，did you want me to get sportsscores，weather，traffic，or news？     
[055]根据本发明的另一个方面，适应性响应生成器315可以使响应成帧，以影响用户用可以被轻松地识别的发声作出应答。例如，用户可以发出“给我提供一些新闻”，而语音用户界面响应可以是“是哪些类别的新闻？Top news stories、国际新闻、政治新闻还是体育新闻？”响应可能是来自用户的非法的发声，如“Top news stories”或“国际新闻”，很可能导致完整的请求。如此，响应可以符合人的对话的协作性特征和天然的人的“模仿”刚刚听到的作为下一个发声的一部分的倾向。此外，当前上下文的知识可以增强响应，以生成更有意义的谈话响应，如在下面的交换中。
 
用户 What′s the weather like in Dallas？
 
语音用户界面 In Dallas，it′s sunny and 90 degrees.
用户 What theaters are showing the movie＂TheFantastic Four＂there？             
语音用户界面 10 theaters in Dallas are showing′The FantasticFour.＂Do you want show times for a particulartheater？                                
[056]使响应成帧也可以根据人的模型，处理错误识别。例如，人经常记住许多最近的发声，特别是当一个或多个以前的发声被错误识别或无法识别时。谈话中的另一个参与者可能限制对错误识别的或无法识别的发声的一部分的纠正，或在随后的发声和/或其他交互中，可以提供线索以指出初始的解释不正确。如此，通过存储和分析多个发声，随着谈话的进行，可以纠正前面的谈话的发声。
[057]根据本发明的另一个方面，适应性响应生成器315可以为用户生成多模或跨模的响应。在一种实现方式中，响应可以识别并控制一个或多个设备和/或接口，用户可以通过使用最方便的任何一种输入方法或输入方法的组合来作出响应。例如，在多模环境中，要求用户利用“Yes”或“No”引导发声的响应也可以可视地显示多种备选方案。
[058]根据本发明的另一个方面，适应性响应生成器315可以纠正谈话的过程，而不会中断会话流。适应性响应生成器315可以基于假设的排序的列表和对应的确定程度，生成智能响应，可以用来纠正谈话的过程，而不会中断会话流。即，尽管智能响应可以合理地“确定”，但是，智能响应有时也可能不正确。尽管现有的语音用户界面趋于不出现常规失误，正常的人之间的交互可以预期出现失误，并相应地进行处理。如此，错误识别之后的响应可以在澄清而不是错误之后模型化，在随后的响应中可以选择词语，以使谈话继续进行，并确定与用户一起探查的适当的域。例如，过程校正可以导致下面的交换：
 
用户 Can you get[mumbled words here]Mariners[more mumbled words]1？              
语音用户界面 I′ve got the score for the Mariners game？4-2Mariners.                              
用户 No，the start time for tomorrow′s game.
语音用户界面 Oh，7:05.
[059]利用特定的示范性实现方式和实施例对本发明进行了描述。然而，所属领域的技术人员将认识到，在不偏离本发明的范围和精神的情况下，可以进行各种修改。因此，说明书和图形都只是示范性的，本发明的范围仅仅由所附的权利要求来确定。

Claims (42)

1.一种用于提供生成对用户发声的协作会话响应的语音用户界面的方法，所述响应基于有关当前谈话和/或一个或多个过去的谈话的共享知识，所述方法包括：
接收输入，所述输入至少包括一个发声；
更新所述共享知识以包括关于所述至少一个发声的知识；
基于所述更新的共享知识，确定所述至少一个发声的预定的含义；以及
基于所述确定的预定的含义生成响应。
2.根据权利要求1所述的方法，其中，更新所述共享知识包括用关于所述至少一个发声的信息填充短期上下文堆栈。
3.根据权利要求2所述的方法，进一步包括在预定的时间量之后，使所述短期上下文堆栈中的关于所述至少一个发声的信息过期。
4.根据权利要求1所述的方法，其中更新所述共享知识包括，基于关于所述至少一个发声的信息，更新一个或多个长期配置文件。
5.根据权利要求1所述的方法，其中，确定所述预定的含义包括标识所述至少一个发声的谈话类型，所述谈话类型基于发声的一个或多个目标，发声的一个或多个参与者角色，和/或发声的信息的分配。
6.根据权利要求1所述的方法，其中，确定所述预定的含义包括生成所述至少一个发声的预定的含义的至少一个假设，所述至少一个假设包括确定程度。
7.根据权利要求6所述的方法，其中，生成所述至少一个假设包括标识所述至少一个发声中的上下文记号和/或限定符。
8.根据权利要求7所述的方法，进一步包括基于所述共享知识推断所述至少一个发声的丢失的上下文记号和/或限定符。
9.根据权利要求8所述的方法，其中，所述至少一个假设的所述确定程度基于一个或多个所述标识的上下文记号和/或限定符，所述推断的上下文记号和/或限定符，和/或生成响应所需的上下文记号和/或限定符。
10.根据权利要求1所述的方法，其中，生成所述响应包括改编所述响应中的上下文记号和/或语法，以反映所述至少一个发声中的上下文记号和/或语法。
11.根据权利要求1所述的方法，其中，生成所述响应包括使所述响应成帧，以影响随后的发声。
12.根据权利要求1所述的方法，进一步包括：
了解所述确定的预定的含义是不正确的；
进一步更新所述共享知识，以包括所述确定的预定含义是不正确的知识；以及
基于所述进一步更新的共享知识，确定所述至少一个发声的第二预定的含义，其中，排除所述不正确的确定的预定的含义作为可能的第二预定的含义。
13.根据权利要求1所述的方法，其中，所述输入是多模输入。
14.根据权利要求1所述的方法，其中，所述响应是多模响应。
15.用于提供生成对用户发声的协作会话响应的语音用户界面的包含计算机可执行的指令的计算机可读取的介质，所述响应基于有关当前谈话和/或一个或多个过去的谈话的共享知识，当执行时，所述计算机可执行的指令用于：
接收输入，所述输入至少包括一个发声；
更新所述共享知识以包括关于所述至少一个发声的知识；
基于所述更新的共享知识，确定所述至少一个发声的预定的含义；以及
基于所述确定的预定的含义生成响应。
16.根据权利要求15所述的计算机可读取的介质，其中，计算机可执行的指令用于，通过用关于所述至少一个发声的信息填充短期上下文堆栈来更新所述共享知识。
17.根据权利要求16所述的计算机可读取的介质，其中，计算机可执行的指令用于在预定的时间量之后，使所述短期上下文堆栈中的关于所述至少一个发声的信息过期。
18.根据权利要求15所述的计算机可读取的介质，其中，计算机可执行的指令用于，通过基于关于所述至少一个发声的信息更新一个或多个长期配置文件来更新所述共享知识。
19.根据权利要求15所述的计算机可读取的介质，其中，所述计算机可执行的指令用于，通过标识所述至少一个发声的谈话类型，确定所述预定的含义，所述谈话类型基于所述发声的一个或多个目标，发声的一个或多个参与者角色，和/或发声的信息的分配。
20.根据权利要求15所述的计算机可读取的介质，其中，所述计算机可执行的指令用于，通过生成所述至少一个发声的预定的含义的至少一个假设来确定所述预定的含义，所述至少一个假设包括确定程度。
21.根据权利要求20所述的计算机可读取的介质，其中，所述计算机可执行的指令用于，通过标识所述至少一个发声中的上下文记号和/或限定符来生成所述至少一个假设。
22.根据权利要求21所述的计算机可读取的介质，其中，所述计算机可执行的指令用于，基于所述共享知识推断所述至少一个发声的丢失的上下文记号和/或限定符。
23.根据权利要求22所述的计算机可读取的介质，其中，所述至少一个假设的所述确定程度基于一个或多个所述标识的上下文记号和/或限定符，所述推断的上下文记号和/或限定符，和/或生成响应所需的上下文记号和/或限定符。
24.根据权利要求15所述的计算机可读取的介质，其中，所述计算机可执行的指令用于，通过改编所述响应中的上下文记号和/或语法以反映所述至少一个发声中的上下文记号和/或语法来生成所述响应。
25.根据权利要求15所述的计算机可读取的介质，其中，所述计算机可执行的指令用于，通过使所述响应成帧以影响随后的发声来生成所述响应。
26.根据权利要求15所述的计算机可读取的介质，其中，当执行时，所述计算机可执行的指令进一步用于：
了解所述确定的预定的含义是不正确的；
进一步更新所述共享知识，以包括所述确定的预定含义是不正确的知识；以及
基于所述进一步更新的共享知识，确定所述至少一个发声的第二预定的含义，其中，排除所述不正确的确定的预定的含义作为可能的第二预定的含义。
27.根据权利要求15所述的计算机可读取的介质，其中，所述输入是多模输入。
28.根据权利要求15所述的计算机可读取的介质，其中，所述响应是多模响应。
29.一种用于提供生成对用户发声的协作会话响应的语音用户界面的系统，所述系统至少包括一个用于存储有关当前谈话和/或一个或多个过去的谈话的共享知识的存储器，所述系统包括：
接收输入的至少一个输入设备，所述输入至少包括一个发声；以及
一个或多个处理器，用于：
更新所述共享知识以包括关于所述至少一个发声的知识；
基于所述更新的共享知识，确定所述至少一个发声的预定的含义；以及
基于所述确定的预定的含义生成响应。
30.根据权利要求29所述的系统，其中，所述一个或多个处理器用于，通过用关于所述至少一个发声的信息填充短期上下文堆栈来更新所述共享知识。
31.根据权利要求30所述的系统，其中，所述一个或多个处理器用于在预定的时间量之后，使所述短期上下文堆栈中的关于所述至少一个发声的信息过期。
32.根据权利要求29所述的系统，其中，所述一个或多个处理器用于，通过基于关于所述至少一个发声的信息更新一个或多个长期配置文件来更新所述共享知识。
33.根据权利要求29所述的系统，其中，所述一个或多个处理器用于，通过标识所述至少一个发声的谈话类型，确定所述预定的含义，所述谈话类型基于所述发声的一个或多个目标，发声的一个或多个参与者角色，和/或发声的信息的分配。
34.根据权利要求29所述的系统，其中，所述一个或多个处理器用于，通过生成所述至少一个发声的预定的含义的至少一个假设来确定所述预定的含义，所述至少一个假设包括确定程度。
35.根据权利要求34所述的系统，其中，所述一个或多个处理器用于，通过标识所述至少一个发声中的上下文记号和/或限定符来生成所述至少一个假设。
36.根据权利要求35所述的系统，其中，所述一个或多个处理器用于，基于所述共享知识推断所述至少一个发声的丢失的上下文记号和/或限定符。
37.根据权利要求36所述的系统，其中，所述至少一个假设的所述确定程度基于一个或多个所述标识的上下文记号和/或限定符，所述推断的上下文记号和/或限定符，和/或生成响应所需的上下文记号和/或限定符。
38.根据权利要求29所述的系统，其中，所述一个或多个处理器用于，通过改编所述响应中的上下文记号和/或语法以反映所述至少一个发声中的上下文记号和/或语法来生成所述响应。
39.根据权利要求29所述的系统，其中，所述一个或多个处理器用于，通过使所述响应成帧以影响随后的发声来生成所述响应。
40.根据权利要求29所述的系统，其中，所述一个或多个处理器进一步用于：
了解所述确定的预定的含义是不正确的；
进一步更新所述共享知识，以包括所述确定的预定含义是不正确的知识；以及
基于所述进一步更新的共享知识，确定所述至少一个发声的第二预定的含义，其中，排除所述不正确的确定的预定的含义作为可能的第二预定的含义。
41.根据权利要求29所述的系统，其中，所述输入是多模输入。
42.根据权利要求29所述的系统，其中，所述响应是多模响应。
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