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    • 1. 发明授权
    • Confidence measure system using a near-miss pattern
    • 使用近似模式的置信度系统
    • US06571210B2
    • 2003-05-27
    • US09192001
    • 1998-11-13
    • Hsiao-Wuen HonAsela J. R. Gunawardana
    • Hsiao-Wuen HonAsela J. R. Gunawardana
    • G10L1506
    • G10L15/08
    • A method and system of performing confidence measure in a speech recognition system includes receiving an utterance of input speech and creating a near-miss pattern or a near-miss list of possible word entries for the utterance. Each word entry includes an associated value of probability that the utterance corresponds to the word entry. The near-miss list of possible word entries is compared with corresponding stored near-miss confidence templates. Each word in the vocabulary (or keyword list) of near-miss confidence template, which includes a list of word entries and each word entry in each list includes an associated value. Confidence measure for a particular hypothesis word is performed based on the comparison of the values in the near-miss list of possible word entries with the values of the corresponding near-miss confidence template.
    • 在语音识别系统中执行置信度测量的方法和系统包括:接收输入语音的发声,并创建用于话语的可能单词条目的接近丢失模式或近似列表。 每个词条目包括发音对应于词条目的概率的相关值。 将可能的词条的近奇列表与相应的存储的近错信度模板进行比较。 近错信号模板的词汇表(或关键字列表)中的每个单词包括一个词条目列表和每个列表中的每个单词条目包括相关联的值。 基于将可能词条近似列表中的值与对应的近错信度模板的值进行比较来执行特定假设词的置信度度量。
    • 2. 发明授权
    • Joint ranking model for multilingual web search
    • 多语言网络搜索的联合排名模型
    • US08326785B2
    • 2012-12-04
    • US12241078
    • 2008-09-30
    • Cheng NiuMing ZhouHsiao-Wuen Hon
    • Cheng NiuMing ZhouHsiao-Wuen Hon
    • G06F17/00G06F17/20G06F7/00G06F17/30G06N5/00
    • G06F17/30675
    • A classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents.
    • 构建分类器至少部分地基于与其他文档的相似性以及这些其他文档与查询的相关性来对查询中发现的不同语言的文档进行排序。 联合排名模型,例如基于玻尔兹曼(Boltzmann)机器,用于表示文档之间的内容相似性,并且帮助确定一组文档的联合相关概率。 因此,利用一种语言的相关文件来改进不同语言文件的相关性估计。 在一个方面,隐藏的单位(神经元)表示检索的文档中的集群(对应于相关主题),输出层表示相关文档及其特征,边缘表示集群和文档之间的关系。
    • 7. 发明授权
    • Method and apparatus for tone-sensitive acoustic modeling
    • 用于音调声学建模的方法和装置
    • US5884261A
    • 1999-03-16
    • US271639
    • 1994-07-07
    • Peter V. de SouzaAdam B. FinebergHsiao-Wuen HonBaosheng Yuan
    • Peter V. de SouzaAdam B. FinebergHsiao-Wuen HonBaosheng Yuan
    • G10L11/04G10L15/02G10L15/14G10L15/18G10L9/00
    • G10L15/144G10L25/15G10L25/90
    • Tone-sensitive acoustic models are generated by first generating acoustic vectors which represent the input data. The input data is separated into multiple frames and an acoustic vector is generated for each frame which represents the input data over its corresponding frame. A tone-sensitive parameter is then generated for each of the frames which indicates the tone of the input data at its corresponding frame. Tone-sensitive parameters are generated in accordance with two embodiments. First, a pitch detector may be used to calculate a pitch for each of the frames. If a pitch cannot be detected for a particular frame, then a pitch is created for that frame based on the pitch values of surrounding frames. Second, the cross covariance between the autocorrelation coefficients for each frame and its successive frame may be generated and used as the tone-sensitive parameter. Feature vectors are then created for each frame by appending the tone-sensitive parameter for a frame to the acoustic vector for the same frame. Then, using these feature vectors, acoustic models are created which represent the input data.
    • 通过首先产生表示输入数据的声矢量来产生音调敏感的声学模型。 输入数据被分成多个帧,并且为代表其对应帧上的输入数据的每个帧生成声向量。 然后,对于指示在其对应帧处的输入数据的音调的每个帧,生成对音调敏感的参数。 根据两个实施例产生音敏参数。 首先,可以使用音调检测器来计算每个帧的音调。 如果对于特定帧不能检测到音调,则基于周围帧的音调值创建针对该帧的音高。 其次,可以生成每个帧及其连续帧的自相关系数之间的交叉协方差,并将其用作音调敏感参数。 然后通过将帧的音调敏感参数附加到相同帧的声矢量来为每个帧创建特征向量。 然后,使用这些特征向量,创建表示输入数据的声学模型。
    • 9. 发明授权
    • Continuous mandarin chinese speech recognition system having an
integrated tone classifier
    • 连续汉语中文语音识别系统具有综合音分类器
    • US5602960A
    • 1997-02-11
    • US316257
    • 1994-09-30
    • Hsiao-Wuen HonYen-Lu ChowKai-Fu Lee
    • Hsiao-Wuen HonYen-Lu ChowKai-Fu Lee
    • G10L15/02G10L15/04G10L15/18G10L3/02
    • G10L15/04G10L25/15
    • A speech recognition system for continuous Mandarin Chinese speech comprises a microphone, an A/D converter, a syllable recognition system, an integrated tone classifier, and a confidence score augmentor. The syllable recognition system generates N-best theories with initial confidence scores. The integrated tone classifier has a pitch estimator to estimate the pitch of the input once and a long-term tone analyzer to segment the estimated pitch according to the syllables of each of the N-best theories. The long-term tone analyzer performs long-term tonal analysis on the segmented, estimated pitch and generates a long-term tonal confidence signal. The confidence score augmentor receives the initial confidence scores and the long-term tonal confidence signals, modifies each initial confidence score according to the corresponding long-term tonal confidence signal, re-ranks the N-best theories according to the augmented confidence scores, and outputs the N-best theories.
    • 用于连续汉语普通话的语音识别系统包括麦克风,A / D转换器,音节识别系统,集成音分类器和置信分数增强器。 音节识别系统产生具有初始置信分数的N最佳理论。 综合音分类器具有估计输入音高的音调估计器和一个长期音调分析器,以根据每个N最佳理论的音节来分段估计音高。 长期音调分析仪对分段估计音高进行长期色调分析,并产生长期色调置信度信号。 信心分数增强器接收初始置信度分数和长期音调信号,根据相应的长期音调信号信号修改每个初始置信度分数,根据增强的置信度得分重新排列N最佳理论; 输出N最好的理论。
    • 10. 发明申请
    • Cost-Per-Action Model Based on Advertiser-Reported Actions
    • 基于广告商报告的动作的每次操作费用模型
    • US20130246167A1
    • 2013-09-19
    • US13421626
    • 2012-03-15
    • Tao QinTie-Yan LiuWenkui DingWei-Ying MaHsiao-Wuen Hon
    • Tao QinTie-Yan LiuWenkui DingWei-Ying MaHsiao-Wuen Hon
    • G06Q30/02
    • G06Q30/0256
    • According to a cost-per-action advertising model, advertisers submit ads with cost-per-action bids. Ad auctions are conducted and winning ads are returned with contextually relevant search results. Each time a winning ad is selected by a user, resulting in the user being redirected to a website associated with the advertiser, a selected impression and a price is recorded for the winning ad. Periodically, an advertiser submits a report indicating a number of actions attributed to the ads that have occurred through the advertiser website. The advertiser is then charged a fee for each reported action based on the recorded prices for the winning ads and based on the number of selected impressions recorded for the winning ads.
    • 根据每次操作费用广告模式,广告客户会按照每次操作费用出价提交广告。 进行广告拍卖,并返回具有内容相关搜索结果的获胜广告。 每当用户选择获胜广告时,导致用户被重定向到与广告商相关联的网站,则为获胜广告记录所选择的展示和价格。 定期地,广告客户会提交一份报告,指示通过广告客户网站发生的广告归因的一些操作。 然后,根据获胜广告的记录价格并根据为获胜广告记录的所选曝光次数,为每个报告的动作收取费用。