公开(公告)号：US20100312556A1

公开(公告)日：2010-12-09

申请号：US12480864

申请日：2009-06-09

Applicant: Andrej LJOLJE ,  Alistair D. CONKIE ,  Ann K. SYRDAL

Inventor： Andrej LJOLJE ,  Alistair D. CONKIE ,  Ann K. SYRDAL

IPC: G10L15/06

CPC classification number: G10L15/07 ,  G10L15/10 ,  G10L15/265

Abstract: Disclosed herein are systems, computer-implemented methods, and tangible computer-readable storage media for speaker recognition personalization. The method recognizes speech received from a speaker interacting with a speech interface using a set of allocated resources, the set of allocated resources including bandwidth, processor time, memory, and storage. The method records metrics associated with the recognized speech, and after recording the metrics, modifies at least one of the allocated resources in the set of allocated resources commensurate with the recorded metrics. The method recognizes additional speech from the speaker using the modified set of allocated resources. Metrics can include a speech recognition confidence score, processing speed, dialog behavior, requests for repeats, negative responses to confirmations, and task completions. The method can further store a speaker personalization profile having information for the modified set of allocated resources and recognize speech associated with the speaker based on the speaker personalization profile.

Abstract translation: 这里公开了用于说话人识别个性化的系统，计算机实现的方法和有形的计算机可读存储介质。 该方法使用一组分配的资源来识别从与语音接口交互的扬声器接收的语音，所分配的资源的集合包括带宽，处理器时间，存储器和存储。 该方法记录与识别的语音相关联的度量，并且在记录度量之后，修改与记录的度量相称的所分配资源集合中的所分配的资源中的至少一个。 该方法使用经修改的分配资源集来识别来自扬声器的附加语音。 指标可以包括语音识别置信度分数，处理速度，对话行为，重复请求，对确认的否定响应以及任务完成。 该方法还可以存储具有用于所修改的分配资源集合的信息的扬声器个性化简档，并且基于说话者个性化简档识别与说话者相关联的语音。

公开(公告)号：US20100145707A1

公开(公告)日：2010-06-10

申请号：US12328407

申请日：2008-12-04

Applicant: Andrej LJOLJE ,  Alistair D. Conkie ,  Ann K. Syrdal

Inventor： Andrej LJOLJE ,  Alistair D. Conkie ,  Ann K. Syrdal

IPC: G10L13/06

CPC classification number: G10L15/187 ,  G10L15/183 ,  G10L2015/025

Abstract: Disclosed herein are systems, computer-implemented methods, and tangible computer-readable media for generating a pronunciation model. The method includes identifying a generic model of speech composed of phonemes, identifying a family of interchangeable phonemic alternatives for a phoneme in the generic model of speech, labeling the family of interchangeable phonemic alternatives as referring to the same phoneme, and generating a pronunciation model which substitutes each family for each respective phoneme. In one aspect, the generic model of speech is a vocal tract length normalized acoustic model. Interchangeable phonemic alternatives can represent a same phoneme for different dialectal classes. An interchangeable phonemic alternative can include a string of phonemes.

Abstract translation: 本文公开了用于生成发音模型的系统，计算机实现的方法和有形的计算机可读介质。 该方法包括识别由音素组成的通用语音模型，在通用语音模型中识别音素的可互换音素替代品系列，将可互换音素替代品的家族标记为指相同的音素，以及生成发音模型，其中 将每个家庭的每个音素替代。 在一个方面，语音的通用模型是声道长度归一化声学模型。 可互换的音素替代品可以代表不同方言课程的相同音素。 可互换的音素替代品可以包括一串音素。

公开(公告)号：US20100312560A1

公开(公告)日：2010-12-09

申请号：US12480848

申请日：2009-06-09

Applicant: Andrej LJOLJE ,  Alistair D. CONKIE ,  Ann K. SYRDAL

Inventor： Andrej LJOLJE ,  Alistair D. CONKIE ,  Ann K. SYRDAL

IPC: G10L15/02

CPC classification number: G10L17/14 ,  G10L15/063 ,  G10L15/07 ,  G10L15/14 ,  G10L15/187 ,  G10L15/265 ,  G10L15/30 ,  G10L2015/025

Abstract: Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for recognizing speech by adapting automatic speech recognition pronunciation by acoustic model restructuring. The method identifies an acoustic model and a matching pronouncing dictionary trained on typical native speech in a target dialect. The method collects speech from a new speaker resulting in collected speech and transcribes the collected speech to generate a lattice of plausible phonemes. Then the method creates a custom speech model for representing each phoneme used in the pronouncing dictionary by a weighted sum of acoustic models for all the plausible phonemes, wherein the pronouncing dictionary does not change, but the model of the acoustic space for each phoneme in the dictionary becomes a weighted sum of the acoustic models of phonemes of the typical native speech. Finally the method includes recognizing via a processor additional speech from the target speaker using the custom speech model.

Abstract translation: 这里公开的是系统，计算机实现的方法和用于通过声学模型重构来适应自动语音识别发音来识别语音的计算机可读存储介质。 该方法识别在目标方言中典型的本地语音训练的声学模型和匹配的发音字典。 该方法从新的演讲者收集演讲，从而收集到的演讲并转录收集的演讲，以产生一个合理的音素格子。 然后，该方法创建一个自定义语音模型，用于通过用于所有似乎合理的音素的声学模型的加权和来表示在发音字典中使用的每个音素，其中发音字典不改变，而是在每个音素的声学空间的模型中 字典成为典型本地语音的音素的声学模型的加权和。 最后，该方法包括使用定制语音模型通过处理器从目标说话者识别附加语音。

公开(公告)号：US20110066433A1

公开(公告)日：2011-03-17

申请号：US12561005

申请日：2009-09-16

Applicant: Andrej LJOLJE ,  Diamantino Antonio CASEIRO ,  Alistair D. CONKIE

Inventor： Andrej LJOLJE ,  Diamantino Antonio CASEIRO ,  Alistair D. CONKIE

IPC: G10L15/00

CPC classification number: G10L15/07 ,  G10L15/04 ,  G10L15/063 ,  G10L15/083 ,  G10L15/14 ,  G10L15/22 ,  G10L15/28 ,  G10L15/32

Abstract: Disclosed herein are methods, systems, and computer-readable storage media for automatic speech recognition. The method includes selecting a speaker independent model, and selecting a quantity of speaker dependent models, the quantity of speaker dependent models being based on available computing resources, the selected models including the speaker independent model and the quantity of speaker dependent models. The method also includes recognizing an utterance using each of the selected models in parallel, and selecting a dominant speech model from the selected models based on recognition accuracy using the group of selected models. The system includes a processor and modules configured to control the processor to perform the method. The computer-readable storage medium includes instructions for causing a computing device to perform the steps of the method.

Abstract translation: 这里公开了用于自动语音识别的方法，系统和计算机可读存储介质。 该方法包括选择一个说话者独立模型，并选择一个说话者依赖模型的数量，说话人依赖模型的数量是基于可用的计算资源，所选择的模型包括与说话者无关的模型和说话者依赖模型的数量。 该方法还包括使用所选择的模型中的每一个并行地识别话语，并且基于使用所选择的模型的组的识别精度从所选择的模型中选择主要语言模型。 该系统包括处理器和被配置为控制处理器执行该方法的模块。 计算机可读存储介质包括用于使计算设备执行该方法的步骤的指令。

公开(公告)号：US20110137653A1

公开(公告)日：2011-06-09

申请号：US12631111

申请日：2009-12-04

Applicant: Andrej LJOLJE ,  Mazin Gilbert

Inventor： Andrej LJOLJE ,  Mazin Gilbert

IPC: G10L15/00

CPC classification number: G10L15/183 ,  G10L2015/227 ,  G10L2015/228

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for performing speech recognition based on a masked language model. A system configured to practice the method receives a masked language model including a plurality of words, wherein a bit mask identifies whether each of the plurality of words is allowed or disallowed with regard to an adaptation subset, receives input speech, generates a speech recognition lattice based on the received input speech using the masked language model, removes from the generated lattice words identified as disallowed by the bit mask for the adaptation subset, and recognizes the received speech based on the lattice. Alternatively during the generation step, the system can only add words indicated as allowed by the bit mask. The bit mask can be separate from or incorporated as part of the masked language model. The system can dynamically update the adaptation subset and bit mask.

Abstract translation: 本文公开了用于基于掩蔽语言模型执行语音识别的系统，方法和计算机可读存储介质。 被配置为实施该方法的系统接收包括多个单词的掩蔽语言模型，其中位掩码识别关于自适应子集是否允许或不允许多个单词中的每一个，接收输入语音，生成语音识别格 基于使用掩蔽语言模型的接收到的输入语音，从由适配子集的位掩码识别为不允许的生成的格子字中移除，并且基于格子识别接收的语音。 或者在生成步骤期间，系统只能添加由位掩码允许的指示的字。 位掩码可以与掩蔽语言模型的一部分分开或并入。 系统可以动态地更新自适应子集和位掩码。

公开(公告)号：US20110137650A1

公开(公告)日：2011-06-09

申请号：US12633334

申请日：2009-12-08

Applicant: Andrej LJOLJE

Inventor： Andrej LJOLJE

IPC: G10L15/14 ,  G10L15/06

CPC classification number: G10L15/144 ,  G10L15/063

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for training adaptation-specific acoustic models. A system practicing the method receives speech and generates a full size model and a reduced size model, the reduced size model starting with a single distribution for each speech sound in the received speech. The system finds speech segment boundaries in the speech using the full size model and adapts features of the speech data using the reduced size model based on the speech segment boundaries and an overall centroid for each speech sound. The system then recognizes speech using the adapted features of the speech. The model can be a Hidden Markov Model (HMM). The reduced size model can also be of a reduced complexity, such as having fewer mixture components than a model of full complexity. Adapting features of speech can include moving the features closer to an overall feature distribution center.

Abstract translation: 本文公开了用于训练适应特定声学模型的系统，方法和计算机可读存储介质。 实施该方法的系统接收语音并生成全尺寸模型和缩小尺寸模型，缩小尺寸模型从接收到的语音中的每个语音的单个分布开始。 该系统使用全尺寸模型在语音中找到语音段边界，并且使用基于语音段边界的缩小尺寸模型和每个语音的整体质心来适应语音数据的特征。 该系统然后使用该语音的适应特征识别语音。 该模型可以是隐马尔可夫模型（HMM）。 缩小的尺寸模型也可以是降低的复杂性，例如具有比完全复杂性的模型更少的混合分量。 适应语音功能可以包括将功能移动到更接近整体功能分配中心。

公开(公告)号：US20110119059A1

公开(公告)日：2011-05-19

申请号：US12618371

申请日：2009-11-13

Applicant: Andrej LJOLJE ,  Bernard S. RENGER ,  Steven Neil TISCHER

Inventor： Andrej LJOLJE ,  Bernard S. RENGER ,  Steven Neil TISCHER

IPC: G10L15/06 ,  G10L15/00 ,  G10L11/00

CPC classification number: G10L15/075 ,  G10L15/063 ,  G10L15/065 ,  G10L15/07 ,  G10L15/08

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for selecting a speech recognition model in a standardized speech recognition infrastructure. The system receives speech from a user, and if a user-specific supervised speech model associated with the user is available, retrieves the supervised speech model. If the user-specific supervised speech model is unavailable and if an unsupervised speech model is available, the system retrieves the unsupervised speech model. If the user-specific supervised speech model and the unsupervised speech model are unavailable, the system retrieves a generic speech model associated with the user. Next the system recognizes the received speech from the user with the retrieved model. In one embodiment, the system trains a speech recognition model in a standardized speech recognition infrastructure. In another embodiment, the system handshakes with a remote application in a standardized speech recognition infrastructure.

Abstract translation: 这里公开了用于在标准化语音识别基础设施中选择语音识别模型的系统，方法和计算机可读存储介质。 系统从用户接收语音，并且如果与用户相关联的用户特定的监督语音模型可用，则检索监督的语音模型。 如果用户特定的监督语音模型不可用，并且如果无人监督的语音模型可用，则系统检索无监督语音模型。 如果用户特定的监督语音模型和无监督语音模型不可用，则系统检索与用户相关联的通用语音模型。 接下来，系统使用所检索的模型识别来自用户的接收到的语音。 在一个实施例中，系统在标准化语音识别基础设施中训练语音识别模型。 在另一个实施例中，系统与标准语音识别基础设施中的远程应用握手。

公开(公告)号：US20110077942A1

公开(公告)日：2011-03-31

申请号：US12570757

申请日：2009-09-30

Applicant: Andrej LJOLJE ,  Diamantino Antonio Caseiro

Inventor： Andrej LJOLJE ,  Diamantino Antonio Caseiro

IPC: G10L15/06

CPC classification number: G10L15/1815 ,  G10L15/063 ,  G10L15/065 ,  G10L15/183 ,  G10L15/22

Abstract: Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for handling expected repeat speech queries or other inputs. The method causes a computing device to detect a misrecognized speech query from a user, determine a tendency of the user to repeat speech queries based on previous user interactions, and adapt a speech recognition model based on the determined tendency before an expected repeat speech query. The method can further include recognizing the expected repeat speech query from the user based on the adapted speech recognition model. Adapting the speech recognition model can include modifying an acoustic model, a language model, and/or a semantic model. Adapting the speech recognition model can also include preparing a personalized search speech recognition model for the expected repeat query based on usage history and entries in a recognition lattice. The method can include retaining unmodified speech recognition models with adapted speech recognition models.

Abstract translation: 本文公开了用于处理预期重复语音查询或其他输入的系统，计算机实现的方法和计算机可读存储介质。 该方法使得计算设备检测来自用户的误识别语音查询，确定用户基于先前用户交互重复语音查询的趋势，以及基于在预期重复语音查询之前确定的趋势来调整语音识别模型。 该方法还可以包括基于适应的语音识别模型识别来自用户的预期重复语音查询。 适应语音识别模型可以包括修改声学模型，语言模型和/或语义模型。 适应语音识别模型还可以包括基于使用历史和识别格中的条目为预期重复查询准备个性化搜索语音识别模型。 该方法可以包括使用适应的语音识别模型保留未修改的语音识别模型。

公开(公告)号：US20100161315A1

公开(公告)日：2010-06-24

申请号：US12343981

申请日：2008-12-24

Applicant: I. Dan MELAMED ,  Yeon-Jun KIM ,  Bernard S. RENGER ,  Andrej LJOLJE ,  David J. SMITH

Inventor： I. Dan MELAMED ,  Yeon-Jun KIM ,  Bernard S. RENGER ,  Andrej LJOLJE ,  David J. SMITH

IPC: G06F17/27 ,  G10L15/26

CPC classification number: G06F17/2715 ,  G10L15/26 ,  G10L17/00

Abstract: A method of correlating received communication data with operational communication characteristics is provided. The method includes receiving audible input from a source in a communication over a communications network, recording the received audible input, and transcribing the recorded audible input into a transcript. The method further includes outputting the transcript, specifying features of the transcript to be analyzed, specifying and recording operational communication characteristics particular to the communication, analyzing the transcript for the specified features to identify patterns associated with the audible input, computing statistical correlations of the identified patterns with the operational communication characteristics, and outputting results of the computed statistical correlations on a user interface.

Abstract translation: 提供了一种使接收到的通信数据与操作通信特性相关的方法。 该方法包括通过通信网络在通信中接收来自源的可听输入，记录所接收的可听输入，以及将记录的可听输入转录成抄本。 该方法还包括输出抄本，指定要分析的抄本的特征，指定和记录特定于通信的操作通信特征，分析指定特征的抄本以识别与可听见输入相关联的模式，计算所识别的 具有操作通信特性的模式，并且在用户界面上输出所计算的统计相关性的结果。

公开(公告)号：US20120290298A1

公开(公告)日：2012-11-15

申请号：US13103665

申请日：2011-05-09

Applicant: Andrej LJOLJE ,  Diamantino Antonio Caseiro ,  Mazin Gilbert ,  Vincent Goffin ,  Taniya Mishra

Inventor： Andrej LJOLJE ,  Diamantino Antonio Caseiro ,  Mazin Gilbert ,  Vincent Goffin ,  Taniya Mishra

IPC: G10L15/26

CPC classification number: G10L15/197 ,  G10L15/063 ,  G10L15/22

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for assigning saliency weights to words of an ASR model. The saliency values assigned to words within an ASR model are based on human perception judgments of previous transcripts. These saliency values are applied as weights to modify an ASR model such that the results of the weighted ASR model in converting a spoken document to a transcript provide a more accurate and useful transcription to the user.

Abstract translation: 这里公开了用于将显着权重分配给ASR模型的单词的系统，方法和非暂时计算机可读存储介质。 分配给ASR模型中的单词的显着性值基于以前的成绩单的人类感知判断。 这些显着性值被用作权重以修改ASR模型，使得将口头文档转换成抄本的加权ASR模型的结果为用户提供更准确和有用的转录。