公开(公告)号：US08332226B1

公开(公告)日：2012-12-11

申请号：US11030923

申请日：2005-01-07

申请人： Mazin G. Rahim ,  Juergen Schroeter

发明人： Mazin G. Rahim ,  Juergen Schroeter

IPC分类号： G10L21/00

CPC分类号： G10L15/30 ,  G10L13/04 ,  G10L15/22 ,  G10L15/285 ,  G10L2015/228

摘要： A system and method for providing a scalable spoken dialog system are disclosed. The method comprises receiving information which may be internal to the system or external to the system and dynamically modifying at least one module within a spoken dialog system according to the received information. The modules may be one or more of an automatic speech recognition, natural language understanding, dialog management and text-to-speech module or engine. Dynamically modifying the module may improve hardware performance or improve a specific caller's speech processing accuracy, for example. The modification of the modules or hardware may also be based on an application or a task, or based on a current portion of a dialog.

摘要翻译： 公开了一种用于提供可扩展语音对话系统的系统和方法。 该方法包括接收可能在系统内部或系统外部的信息，并根据所接收的信息动态修改口语对话系统内的至少一个模块。 模块可以是自动语音识别，自然语言理解，对话管理和文本到语音模块或引擎中的一个或多个。 例如，动态修改模块可以改善硬件性能或提高特定呼叫者的语音处理精度。 模块或硬件的修改也可以基于应用或任务，或者基于对话的当前部分。

公开(公告)号：US20120239383A1

公开(公告)日：2012-09-20

申请号：US13481031

申请日：2012-05-25

申请人： Srinivas Bangalore ,  Narendra K. Gupta ,  Mazin G. Rahim

发明人： Srinivas Bangalore ,  Narendra K. Gupta ,  Mazin G. Rahim

IPC分类号： G06F17/27 ,  G10L15/00

CPC分类号： G10L15/1815 ,  G06F17/2785 ,  G10L13/043 ,  G10L15/02 ,  G10L15/1822 ,  G10L15/265

摘要： A system and method are disclosed that improve automatic speech recognition in a spoken dialog system. The method comprises partitioning speech recognizer output into self-contained clauses, identifying a dialog act in each of the self-contained clauses, qualifying dialog acts by identifying a current domain object and/or a current domain action, and determining whether further qualification is possible for the current domain object and/or current domain action. If further qualification is possible, then the method comprises identifying another domain action and/or another domain object associated with the current domain object and/or current domain action, reassigning the another domain action and/or another domain object as the current domain action and/or current domain object and then recursively qualifying the new current domain action and/or current object. This process continues until nothing is left to qualify.

摘要翻译： 公开了一种提高口语对话系统中的自动语音识别的系统和方法。 该方法包括将语音识别器输出划分为独立子句，识别每个自包含子句中的对话行为，通过识别当前域对象和/或当前域动作进行限定对话行为，以及确定是否可进行进一步的限定 对于当前域对象和/或当前域操作。 如果可以进一步鉴定，则该方法包括识别与当前域对象和/或当前域操作相关联的另一域操作和/或另一域对象，将另一域操作和/或另一域对象重新分配为当前域操作，以及 /或当前域对象，然后递归地限定新的当前域操作和/或当前对象。 这个过程一直持续到没有什么是剩下的资格。

公开(公告)号：US08249879B2

公开(公告)日：2012-08-21

申请号：US13290501

申请日：2011-11-07

申请人： Srinivas Bangalore ,  Junlan Feng ,  Mazin G. Rahim

发明人： Srinivas Bangalore ,  Junlan Feng ,  Mazin G. Rahim

IPC分类号： G10L15/18 ,  G06F17/27

CPC分类号： G10L13/08 ,  G06F17/27 ,  G10L15/063 ,  G10L15/183 ,  G10L15/22 ,  H04M3/4936

摘要： Disclosed is a system and method for training a spoken dialog service component from website data. Spoken dialog service components typically include an automatic speech recognition module, a language understanding module, a dialog management module, a language generation module and a text-to-speech module. The method includes converting data from a structured database associated with a website to a structured text data set and a structured task knowledge base, extracting linguistic items from the structured database, and training a spoken dialog service component using at least one of the structured text data, the structured task knowledge base, or the linguistic items. The system includes modules configured to implement the method.

摘要翻译： 公开了一种用于从网站数据训练口语对话服务组件的系统和方法。 口语对话服务组件通常包括自动语音识别模块，语言理解模块，对话管理模块，语言生成模块和文本到语音模块。 该方法包括将来自与网站相关联的结构化数据库的数据转换为结构化文本数据集和结构化任务知识库，从结构化数据库中提取语言项目，以及使用至少一个结构化文本数据来训练口语对话服务组件 ，结构化任务知识库或语言项目。 该系统包括配置为实现该方法的模块。

公开(公告)号：US20120053930A1

公开(公告)日：2012-03-01

申请号：US13290501

申请日：2011-11-07

申请人： Srinivas Bangalore ,  Junlan Feng ,  Mazin G. Rahim

发明人： Srinivas Bangalore ,  Junlan Feng ,  Mazin G. Rahim

IPC分类号： G06F17/27

CPC分类号： G10L13/08 ,  G06F17/27 ,  G10L15/063 ,  G10L15/183 ,  G10L15/22 ,  H04M3/4936

摘要： Disclosed is a system and method for training a spoken dialog service component from website data. Spoken dialog service components typically include an automatic speech recognition module, a language understanding module, a dialog management module, a language generation module and a text-to-speech module. The method includes converting data from a structured database associated with a website to a structured text data set and a structured task knowledge base, extracting linguistic items from the structured database, and training a spoken dialog service component using at least one of the structured text data, the structured task knowledge base, or the linguistic items. The system includes modules configured to implement the method.

公开(公告)号：US07752036B2

公开(公告)日：2010-07-06

申请号：US12344815

申请日：2008-12-29

申请人： Richard Vandervoort Cox ,  Stephen Michael Marcus ,  Mazin G. Rahim ,  Nambirajan Seshadri ,  Robert Douglas Sharp

发明人： Richard Vandervoort Cox ,  Stephen Michael Marcus ,  Mazin G. Rahim ,  Nambirajan Seshadri ,  Robert Douglas Sharp

IPC分类号： G10L19/00

CPC分类号： G10L15/02 ,  G10L15/20

摘要： Recognizing a stream of speech received as speech vectors over a lossy communications link includes constructing for a speech recognizer a series of speech vectors from packets received over a lossy packetized transmission link, wherein some of the packets associated with each speech vector are lost or corrupted during transmission. Each constructed speech vector is multi-dimensional and includes associated features. After waiting for a predetermined time, speech vectors are generated and potentially corrupted features within the speech vector are indicated to the speech recognizer when present. Speech recognition is attempted at the speech recognizer on the speech vectors when corrupted features are present. This recognition may be based only on certain or valid features within each speech vector. Retransmission of a missing or corrupted packet is requested when corrupted values are indicated by the indicating step and when the attempted recognition step fails.

摘要翻译： 识别通过有损通信链路作为语音向量接收的语音流包括：通过有损分组化传输链路从分组接收的分组来构建语音识别器的一系列语音向量，其中与每个语音向量相关联的一些分组丢失或损坏 传输。 每个构造的语音向量是多维的并且包括相关联的特征。 在等待预定的时间之后，产生语音向量，并且在存在时将语音向量内潜在的损坏的特征指示给语音识别器。 当存在损坏的特征时，语音识别器在语音向量上尝试语音识别。 该识别可以仅基于每个语音向量内的某些或有效特征。 当指示步骤指示损坏的值以及尝试的识别步骤失败时，请求重新发送丢失或损坏的数据包。

公开(公告)号：US20090112585A1

公开(公告)日：2009-04-30

申请号：US12344815

申请日：2008-12-29

申请人： Richard Vandervoort Cox ,  Stephen Michael Marcus ,  Mazin G. Rahim ,  Nambirajan Seshadri ,  Robert Douglas Sharp

发明人： Richard Vandervoort Cox ,  Stephen Michael Marcus ,  Mazin G. Rahim ,  Nambirajan Seshadri ,  Robert Douglas Sharp

IPC分类号： G10L15/00

CPC分类号： G10L15/02 ,  G10L15/20

摘要： Recognizing a stream of speech received as speech vectors over a lossy communications link includes constructing for a speech recognizer a series of speech vectors from packets received over a lossy packetized transmission link, wherein some of the packets associated with each speech vector are lost or corrupted during transmission. Each constructed speech vector is multi-dimensional and includes associated features. After waiting for a predetermined time, speech vectors are generated and potentially corrupted features within the speech vector are indicated to the speech recognizer when present. Speech recognition is attempted at the speech recognizer on the speech vectors when corrupted features are present. This recognition may be based only on certain or valid features within each speech vector. Retransmission of a missing or corrupted packet is requested when corrupted values are indicated by the indicating step and when the attempted recognition step fails.

摘要翻译： 识别通过有损通信链路作为语音向量接收的语音流包括：通过有损分组化传输链路接收的分组来构建语音识别器的一系列语音向量，其中与每个语音向量相关联的一些分组丢失或损坏 传输。 每个构造的语音向量是多维的并且包括相关联的特征。 在等待预定的时间之后，产生语音向量，并且在存在时将语音向量内潜在的损坏的特征指示给语音识别器。 当存在损坏的特征时，语音识别器在语音向量上尝试语音识别。 该识别可以仅基于每个语音向量内的某些或有效特征。 当指示步骤指示损坏的值以及尝试的识别步骤失败时，请求重新发送丢失或损坏的数据包。

公开(公告)号：US07280965B1

公开(公告)日：2007-10-09

申请号：US10407565

申请日：2003-04-04

申请人： Lee Begeja ,  Richard Vandervoort Cox ,  Harris Drucker ,  David Crawford Gibbon ,  Allen Louis Gorin ,  Patrick Guy Haffner ,  Steven H. Lewis ,  Zhu Liu ,  Mazin G. Rahim ,  Bernard S. Renger ,  Behzad Shahraray

发明人： Lee Begeja ,  Richard Vandervoort Cox ,  Harris Drucker ,  David Crawford Gibbon ,  Allen Louis Gorin ,  Patrick Guy Haffner ,  Steven H. Lewis ,  Zhu Liu ,  Mazin G. Rahim ,  Bernard S. Renger ,  Behzad Shahraray

IPC分类号： G10L21/00

CPC分类号： G10L13/08 ,  G10L15/18

摘要： Systems and methods for monitoring labelers of speech data. To test or train labelers, a labeler is presented with utterances that have already been identified as belonging to a particular class or call type. The labeler is asked to assign a call type to the utterances. The performance of the labeler is measured by comparing the call types assigned by the labeler with the existing call types of the utterances. The performance of a labeler can also be monitored as the labeler labels speech data by occasionally having the labeler label an utterance that is already labeled and by storing the results.

摘要翻译： 用于监控语音数据标签器的系统和方法。 为了测试或训练标签商，标签器被呈现已经被识别为属于特定类别或呼叫类型的话语。 要求标签器为话语分配一个呼叫类型。 标签器的性能是通过将标签器分配的呼叫类型与话语的现有呼叫类型进行比较来测量的。 标签器的性能也可以被监视，因为标签器通过偶尔将标签标签标记为已经被标记的话语并且通过存储结果来标记语音数据。

公开(公告)号：US06775652B1

公开(公告)日：2004-08-10

申请号：US09107784

申请日：1998-06-30

申请人： Richard Vandervoort Cox ,  Stephen Michael Marcus ,  Mazin G. Rahim ,  Nambirajan Seshadri ,  Robert Douglas Sharp

发明人： Richard Vandervoort Cox ,  Stephen Michael Marcus ,  Mazin G. Rahim ,  Nambirajan Seshadri ,  Robert Douglas Sharp

IPC分类号： G10L1528

CPC分类号： G10L15/02 ,  G10L15/20

摘要： Recognizing a stream of speech received as speech vectors over a lossy communications link includes constructing for a speech recognizer a series of speech vectors from packets received over a lossy packetized transmission link, wherein some of the packets associated with each speech vector are lost or corrupted during transmission. Each constructed speech vector is multi-dimensional and includes associated features. Potentially corrupted features within the speech vector are indicated to the speech recognizer when present. Speech recognition is attempted at the speech recognizer on the speech vectors when corrupted features are present. This recognition may be based only on certain or valid features within each speech vector. Retransmission of a missing or corrupted packet is requested when corrupted values are indicated by the indicating step and when the attempted recognition step fails.

摘要翻译： 识别通过有损通信链路作为语音向量接收的语音流包括：通过有损分组化传输链路从分组接收的分组来构建语音识别器的一系列语音向量，其中与每个语音向量相关联的一些分组丢失或损坏 传输。 每个构造的语音向量是多维的并且包括相关联的特征。 语音向量中的潜在损坏的特征在存在时被指示给语音识别器。 当存在损坏的特征时，语音识别器在语音向量上尝试语音识别。 该识别可以仅基于每个语音向量内的某些或有效特征。 当指示步骤指示损坏的值以及尝试的识别步骤失败时，请求重新发送丢失或损坏的数据包。

公开(公告)号：US5806029A

公开(公告)日：1998-09-08

申请号：US528821

申请日：1995-09-15

申请人： Eric Rolfe Buhrke ,  Wu Chou ,  Mazin G. Rahim

发明人： Eric Rolfe Buhrke ,  Wu Chou ,  Mazin G. Rahim

IPC分类号： G10L15/02 ,  G10L15/14 ,  G10L15/20 ,  G10L5/00

CPC分类号： G10L15/144 ,  G10L15/02 ,  G10L21/0272

摘要： Hierarchical signal bias removal (HSBR) signal conditioning uses a codebook constructed from the set of recognition models and is updated as the recognition models are modified during recognition model training. As a result, HSBR signal conditioning and recognition model training are based on the same set of recognition model parameters, which provides significant reduction in recognition error rate for the speech recognition system.

摘要翻译： 分级信号偏移去除（HSBR）信号调理使用由该组识别模型构建的码本，并且在识别模型训练期间识别模型被修改时被更新。 因此，HSBR信号调理和识别模型训练基于相同的识别模型参数集，这显着降低了语音识别系统的识别误码率。

公开(公告)号：US5590242A

公开(公告)日：1996-12-31

申请号：US217035

申请日：1994-03-24

申请人： Biing-Hwang Juang ,  Mazin G. Rahim

发明人： Biing-Hwang Juang ,  Mazin G. Rahim

IPC分类号： G10L15/14 ,  G10L15/00 ,  G10L15/02 ,  G10L15/20 ,  G10L19/00 ,  G10L21/02 ,  G10L3/02 ,  G10L5/06 ,  G10L9/00

CPC分类号： G10L15/20 ,  G10L15/02 ,  G10L15/142 ,  G10L21/0232

摘要： A signal bias removal (SBR) method based on the maximum likelihood estimation of the bias for minimizing undesirable effects in speech recognition systems is described. The technique is readily applicable in various architectures including discrete (vector-quantization based), semicontinuous and continuous-density Hidden Markov Model (HMM) systems. For example, the SBR method can be integrated into a discrete density HMM and applied to telephone speech recognition where the contamination due to extraneous signal components is unknown. To enable real-time implementation, a sequential method for the estimation of the bias (SSBR) is disclosed.

摘要翻译： 描述了基于用于最小化语音识别系统中的不良影响的偏差的最大似然估计的信号偏移去除（SBR）方法。 该技术易于应用于各种架构，包括离散（矢量量化），半连续和连续密度隐马尔可夫模型（HMM）系统。 例如，SBR方法可以集成到离散密度HMM中，并应用于电话语音识别，其中由于外部信号分量引起的污染是未知的。 为了实现实时性，公开了用于估计偏差（SSBR）的顺序方法。