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    • 3. 发明授权
    • Method and apparatus for generating speech pattern templates
    • 用于生成语音模式模板的方法和装置
    • US4454586A
    • 1984-06-12
    • US322748
    • 1981-11-19
    • Frank C. PirzLawrence R. RabinerJay G. Wilpon
    • Frank C. PirzLawrence R. RabinerJay G. Wilpon
    • G10L11/00G10L15/02G10L15/12G10L1/00
    • G10L15/12
    • A system for generating speech pattern templates for use with either speech recognition or speech synthesis. Reference demisyllable templates are first generated from a reference first speaker using both manual and automatic analysis. The analysis for a second speaker is simplified and automated by comparing with the first speaker's templates. The second speaker speaks the same words at a rate time-warped to match the first speakers rate and template. We define a demisyllable as each of the two halves of a syllable, assuming a syllable starts and ends with a noisy consonant, and the syllable is split at its vowel center, thereby simplifying concatenation and comparison. Key features of the invention include generating a set of signals representative of the time alignment between the first and second speaker's templates, and the time-of-occurence boundaries of each syllable in a word.
    • 一种用于产生用于语音识别或语音合成的语音模式模板的系统。 首先使用手动和自动分析从参考的第一个扬声器生成参考分解模板。 通过与第一个演讲人的模板进行比较,简化和自动化第二个演讲者的分析。 第二个发言人以相同的时间说出一致的话,以匹配第一个演讲者的速度和模板。 我们将一个分音节定义为音节的两个半部分,假设音节开始和结尾是一个嘈杂的辅音,并且音节在其元音中心分裂,从而简化了连接和比较。 本发明的主要特征包括产生一组代表第一和第二说话者模板之间的时间对准的信号以及一个单词中每个音节的发生时间边界。
    • 7. 发明授权
    • Speech recognition employing key word modeling and non-key word modeling
    • 语音识别采用关键词建模和非关键词建模
    • US5509104A
    • 1996-04-16
    • US132430
    • 1993-10-06
    • Chin H. LeeLawrence R. RabinerJay G. Wilpon
    • Chin H. LeeLawrence R. RabinerJay G. Wilpon
    • G10L15/00G10L15/14G10L5/00
    • G10L15/142G10L2015/088
    • Speaker independent recognition of small vocabularies, spoken over the long distance telephone network, is achieved using two types of models, one type for defined vocabulary words (e.g., collect, calling-card, person, third-number and operator), and one type for extraneous input which ranges from non-speech sounds to groups of non-vocabulary words (e.g. `I want to make a collect call please`). For this type of key word spotting, modifications are made to a connected word speech recognition algorithm based on state-transitional (hidden Markov) models which allow it to recognize words from a pre-defined vocabulary list spoken in an unconstrained fashion. Statistical models of both the actual vocabulary words and the extraneous speech and background noises are created. A syntax-driven connected word recognition system is then used to find the best sequence of extraneous input and vocabulary word models for matching the actual input speech.
    • 使用两种类型的模型来实现对长途电话网络上的小词汇的独立识别,一种用于定义的词汇单词(例如,收集,呼叫卡,人,第三号码和运营商)的模型,以及一种类型 对于从非语音声音到非词汇单词组(例如“我想要收集电话”)的无关输入。 对于这种类型的关键词发现,对基于状态转换(隐马尔科夫)模型的连接词语音识别算法进行修改,这允许其识别来自以无限制方式说明的预定义词汇列表中的单词。 创建实际词汇单词和无关语音和背景噪声的统计模型。 然后使用语法驱动的连接词识别系统来找到用于匹配实际输入语音的外来输入和词汇词模型的最佳序列。