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公开(公告)号：US5165007A
公开(公告)日：1992-11-17
申请号：US366231
申请日：1989-06-12
CPC分类号： G10L15/142 , G10L2015/0631
摘要： In a speech recognition system, apparatus and method for modelling words with label-based Markov models is disclosed. The modelling includes: entering a first speech input, corresponding to words in a vocabulary, into an acoustic processor which converts each spoken word into a sequence of standard labels, where each standard label corresponds to a sound type assignable to an interval of time; representing each standard label as a probabilistic model which has a plurality of states, at least one transition from a state to a state, and at least one settable output probability at some transitions; entering selected acoustic inputs into an acoustic processor which converts the selected acoustic inputs into personalized labels, each personalized label corresponding to a sound type assigned to an interval of time; and setting each output probability as the probability of the standard label represented by a given model producing a particular personalized label at a given transition in the given model. The present invention addresses the problem of generating models of words simply and automatically in a speech recognition system.
摘要翻译： 在一种语音识别系统中，公开了用基于标签的马尔可夫模型对词进行建模的装置和方法。 所述建模包括：将对应于词汇表中的单词的第一语音输入输入到将每个口语单词转换成标准标签序列的声学处理器，其中每个标准标签对应于可分配到时间间隔的声音类型; 将每个标准标签表示为具有多个状态的概率模型，至少一个从状态到状态的转变，以及在某些转换时的至少一个可设置的输出概率; 将选定的声音输入输入到将所选择的声音输入转换成个性化标签的声学处理器，每个个性化标签对应于分配给一段时间的声音类型; 并将每个输出概率设置为由给定模型表示的标准标签的概率，该给定模型在给定模型中的给定转换处产生特定个性化标签。 本发明解决了在语音识别系统中简单和自动地生成单词模型的问题。
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2.发明授权Automatic generation of simple Markov model stunted baseforms for words in a vocabulary 失效自动生成简单的马尔科夫模型，使词汇中的词语发生阻塞
公开(公告)号：US4833712A
公开(公告)日：1989-05-23
申请号：US738934
申请日：1985-05-29
CPC分类号： G10L15/14 , G10L15/063
摘要： In a system that (i) defines each word in a vocabulary by a fenemic baseform of fenemic phones, (ii) defines an alphabet of composite phones each of which corresponds to at least one fenemic phone, and (iii) generates a string of fenemes in response to speech input, the method provides for converting a word baseform comprised of fenemic phones into a stunted word baseform of composite phones by (a) replacing each fenemic phone in the fenemic phone word baseform by the composite phone corresponding thereto; and (b) merging together at least one pair of adjacent composite phones by a single composite phone where the adverse effect of the merging is below a predefined threshold.
摘要翻译： 在一个系统中，（i）通过无线电手机的基本形式来定义词汇表中的每个单词，（ii）定义了复合电话的字母表，每个单词对应于至少一个美式手机，以及（iii）生成一串拼音 响应于语音输入，该方法通过以下方式提供：将由短信电话组成的单词基础形式转换成复合电话的发音失真的基本形式：（a）通过与之相对应的复合电话替换所述手机基本形式中的每一个无线电话; 和（b）通过单个复合电话将至少一对相邻复合电话合并在一起，其中合并的不利影响低于预定阈值。
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3.发明授权Constructing Markov model word baseforms from multiple utterances by concatenating model sequences for word segments 失效通过连接字段的模型序列，从多个语句构建马尔科夫模型词基础
公开(公告)号：US4819271A
公开(公告)日：1989-04-04
申请号：US133719
申请日：1987-12-16
CPC分类号： G10L15/144
摘要： The present invention relates to apparatus and method for segmenting multiple utterances of a vocabulary word in a consistent and coherent manner and determining a Markov model sequence for each segment. A fenemic Markov model corresponds to each label.
摘要翻译： 本发明涉及用于以一致和一致的方式分割词汇单词的多个话语的装置和方法，并且确定每个段的马尔可夫模型序列。 模型马可夫模型对应于每个标签。
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公开(公告)号：US4759068A
公开(公告)日：1988-07-19
申请号：US738933
申请日：1985-05-29
CPC分类号： G10L15/14
摘要： Speech recognition is improved by splitting each feneme string at a consistent point into a left portion and a right portion. The present invention addresses the problem of constructing fenemic baseforms which take into account variations in pronunciation of words from one utterance thereof to another. Specifically, the invention relates to a method of constructing a fenemic baseform for a word in a vocabulary of word segments including the steps of: (a) transforming multiple utterances of the word into respective strings of fenemes; (b) defining a set of fenemic Markov model phone machines; (c) determining the best single phone machine P.sub.1 for producing the multiple feneme strings; (d) determining the best two phone baseform of the form P.sub.1 P.sub.2 or P.sub.2 P.sub.1 for producing the multiple feneme strings; (e) aligning the best two phone baseform against each feneme string; (f) splitting each feneme string into a left portion and a right portion with the left portion corresponding to the first phone machine of the two phone baseform and the right portion corresponding to the second phone machine of the two phone baseform; (g) identifying each left portion as a left substring and each right portion as a right substring; (h) processing the set of left substrings and the set of right substrings in the same manner as the set of feneme strings corresponding to the multiple utterances including the further step of inhibiting further splitting of a substring when the single phone baseform thereof has a higher probability of producing the substring than does the best two phone baseform; and (k) concatenating the unsplit single phones in an order corresponding to the order of the feneme substrings to which they correspond.
摘要翻译： 通过将一致点处的每个非对称串分成左部分和右部分来改善语音识别。 本发明解决了考虑到从一个发音到另一个发音的词的发音的变化的构建快速基本形式的问题。 具体地说，本发明涉及一种在词段词汇中构建单词的构象基础形式的方法，包括以下步骤：（a）将单词的多个话语转换成各自的拼写字符串; （b）定义一套美式马尔可夫模型电话机; （c）确定最好的单机P1用于产生多个无线串; （d）确定形式为P1P2或P2P1的最佳两个手机基本形式，用于产生多个拼接线; （e）将最佳的两个手机基本格局对准每个拼音字符串; （f）将每个拼音串分成左侧部分和右侧部分，左侧部分对应于两个电话基本形式的第一电话机和对应于两个电话基本形式的第二电话机的右部分; （g）将每个左部分识别为左子串，每个右部分作为右子串; （h）以与多重话语对应的组合字符串的集合相同的方式处理左子串和右子集的集合，包括当单个电话基础具有更高的子字符串时进一步分割子串的另一步骤 生成子串的概率比最好的两个手机基本形式; 和（k）以对应于它们对应的无限子串的顺序的顺序连接非分开的单个电话。
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5.发明授权Speech recognition employing a set of Markov models that includes Markov models representing transitions to and from silence 失效语音识别采用一组马尔可夫模型，其中包括表示从沉默转换到沉默的马尔可夫模型
公开(公告)号：US4977599A
公开(公告)日：1990-12-11
申请号：US289447
申请日：1988-12-15
CPC分类号： G10L15/02 , G10L15/142 , G10L2015/0631
摘要： Apparatus and method for constructing word baseforms which can be matched against a string of generated acoustic labels. A set of phonetic phone machines are formed, wherein each phone machine has (i) a plurality of states, (ii) a plurality of transitions each of which extends from a state to a state, (iii) a stored probability for each transition, and (iv) stored label output probabilities, each label output probability corresponding to the probability of each phone machine producing a corresponding label. The set of phonetic machines is formed to include a subset of onset phone machines. The stored probabilities of each onset phone macine correspond to at least one phonetic element being uttered at the beginning of a speech segment. The set of phonetic machines is formed to include a subset of trailing phone machines. The stored probabilities of each trailing phone machine correspond to at least one single phonetic element being uttered at the end of a speech segment. Word baseforms are constructed by concatenating phone machines selected from the set.
摘要翻译： 用于构建可与一串生成的声学标签匹配的字基形式的装置和方法。 形成一组语音电话机，其中每个电话机具有（i）多个状态，（ii）多个转换，每个转换从状态延伸到状态，（iii）每个转换的存储概率， 和（iv）存储的标签输出概率，每个标签输出概率对应于每个电话机产生相应标签的概率。 语音机的组合形成为包括起动电话机的一个子集。 每个起始电话机的存储概率对应于在语音段开始时发出的至少一个语音元素。 该组语音机器被形成为包括拖尾电话机的子集。 每个拖尾电话机的存储概率对应于在语音段结束时发出的至少一个单个语音元素。 字基础是通过连接从集合中选择的电话机构成的。
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6.发明授权Fast algorithm for deriving acoustic prototypes for automatic speech recognition 失效用于自动语音识别的声学原型的快速算法
公开(公告)号：US5276766A
公开(公告)日：1994-01-04
申请号：US730714
申请日：1991-07-16
CPC分类号： G10L15/063
摘要： An apparatus for generating a set of acoustic prototype signals for encoding speech includes a memory for storing a training script model comprising a series of word-segment models. Each word-segment model comprises a series of elementary models. An acoustic measure is provided for measuring the value of at least one feature of an utterance of the training script during each of a series of time intervals to produce a series of feature vector signals representing the feature values of the utterance. An acoustic matcher is provided for estimating at least one path through the training script model which would produce the entire series of measured feature vector signals. From the estimated path, the elementary model in the training script model which would produce each feature vector signal is estimated. The apparatus further comprises a cluster processor for clustering the feature vector signals into a plurality of clusters. Each feature vector signal in a cluster corresponds to a single elementary model in a single location in a single word-segment model. Each cluster signal has a cluster value equal to an average of the feature values of all feature vectors in the signal. Finally, the apparatus includes a memory for storing a plurality of prototype vector signals. Each prototype vector signal corresponds to an elementary model, has an identifier, and comprises at least two partition values. The partition values are equal to combinations of the cluster values of one or more cluster signals corresponding to the elementary model.
摘要翻译： 一种用于生成用于编码语音的声原型信号的集合的装置包括用于存储包括一系列字段模型的训练脚本模型的存储器。 每个单词段模型包括一系列基本模型。 提供了一种声学测量，用于在一系列时间间隔的每一个期间测量训练脚本的发音的至少一个特征的值，以产生表示发音的特征值的一系列特征向量信号。 提供声学匹配器用于估计通过训练脚本模型的至少一个路径，其将产生整个测量的特征向量信号的一系列。 从估计的路径，估计将产生每个特征向量信号的训练脚本模型中的基本模型。 该装置还包括用于将特征向量信号聚类成多个聚类的聚类处理器。 群集中的每个特征向量信号对应于单个单词段模型中单个位置中的单个基本模型。 每个聚类信号具有等于信号中所有特征向量的特征值的平均值的聚类值。 最后，该装置包括用于存储多个原型矢量信号的存储器。 每个原型矢量信号对应于基本模型，具有标识符，并且包括至少两个分区值。 分区值等于对应于基本模型的一个或多个聚类信号的聚类值的组合。
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7.发明授权
公开(公告)号：US5033087A
公开(公告)日：1991-07-16
申请号：US323479
申请日：1989-03-14
CPC分类号： G10L15/14
摘要： A continuous speech recognition system includes an automatic phonological rules generator which determines variations in the pronunciation of phonemes based on the context in which they occur. This phonological rules generator associates sequences of labels derived from vocalizations of a training text with respective phonemes inferred from the training text. These sequences are then annotated with their pheneme context from the training text and clustered into groups representing similar pronunciations of each phoneme. A decision tree is generated using the context information of the sequences to predict the clusters to which the sequences belong. The training data is processed by the decision tree to divide the sequences into leaf-groups representing similar pronunciations of each phoneme. The sequences in each leaf-group are clustered into sub-groups representing respectively different pronunciations of their corresponding phoneme in a give context. A Markov model is generated for each sub-group. The various Markov models of a leaf-group are combined into a single compound model by assigning common initial and final states to each model. The compound Markov models are used by a speech recognition system to analyze an unknown sequence of labels given its context.
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8.发明授权Determination of phone weights for markov models in a speech recognition system 失效确定语音识别系统中马尔科夫模型的手机权重
公开(公告)号：US4741036A
公开(公告)日：1988-04-26
申请号：US696976
申请日：1985-01-31
CPC分类号： G10L15/144
摘要： In a speech recognition system, discrimination between similar-sounding uttered words is improved by weighting the probability vector data stored for the Markov model representing the reference word sequence of phones. The weighting vector is derived for each reference word by comparing similar sounding utterances using Viterbi alignment and multivariate analysis which maximizes the differences between correct and incorrect recognition multivariate distributions.
摘要翻译： 在语音识别系统中，通过对表示电话的参考字序列的马尔可夫模型存储的概率向量数据进行加权来改进类似声音发音字之间的区别。 通过使用维特比对齐和多变量分析比较类似的声音语音，为每个参考词导出权重向量，从而最大化正确和不正确的识别多变量分布之间的差异。
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