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11. 发明授权

US5510981A Language translation apparatus and method using context-based translation models 失效
标题翻译：使用基于上下文的翻译模型的语言翻译设备和方法
公开(公告)号：US5510981A
公开(公告)日：1996-04-23
申请号：US144913
申请日：1993-10-28
申请人： Adam L. Berger , Peter F. Brown , Stephen A. Della Pietra , Vincent J. Della Pietra , Andrew S. Kehler , Robert L. Mercer
发明人： Adam L. Berger , Peter F. Brown , Stephen A. Della Pietra , Vincent J. Della Pietra , Andrew S. Kehler , Robert L. Mercer
IPC分类号： G06F17/28
CPC分类号： G06F17/2818 , G06F17/2827
摘要： An apparatus for translating a series of source words in a first language to a series of target words in a second language. For an input series of source words, at least two target hypotheses, each including a series of target words, are generated. Each target word has a context comprising at least one other word in the target hypothesis. For each target hypothesis, a language model match score including an estimate of the probability of occurrence of the series of words in the target hypothesis. At least one alignment connecting each source word with at least one target word in the target hypothesis is identified. For each source word and each target hypothesis, a word match score including an estimate of the conditional probability of occurrence of the source word, given the target word in the target hypothesis which is connected to the source word and given the context in the target hypothesis of the target word which is connected to the source word. For each target hypothesis, a translation match score including a combination of the word match scores for the target hypothesis and the source words in the input series of source words. A target hypothesis match score including a combination of the language model match score for the target hypothesis and the translation match score for the target hypothesis. The target hypothesis having the best target hypothesis match score is output.
摘要翻译：一种用于将第一语言的一系列源词翻译为第二语言的一系列目标词的装置。对于一个输入序列的源字，产生至少两个目标假设，每个包含一系列目标词。每个目标词具有包含目标假设中的至少一个其他单词的上下文。对于每个目标假设，语言模型匹配得分，包括目标假设中的一系列单词的发生概率的估计。识别将每个源字与目标假设中的至少一个目标字连接的至少一个对齐。对于每个源词和每个目标假设，给出包括源词出现的条件概率的估计的词匹配分数，给出连接到源词并且给定目标假设的上下文的目标假设中的目标词连接到源字的目标字。对于每个目标假设，翻译匹配分数包括目标假设的词匹配分数与输入源词序列中的源词组合。目标假设匹配得分，包括目标假设的语言模型匹配分数与目标假设的翻译匹配得分的组合。输出具有最佳目标假设匹配得分的目标假设。

12. 发明授权

US5033087A Method and apparatus for the automatic determination of phonological rules as for a continuous speech recognition system 失效
标题翻译：用于连续语音识别系统自动确定语音规则的方法和装置
公开(公告)号：US5033087A
公开(公告)日：1991-07-16
申请号：US323479
申请日：1989-03-14
申请人： Lalit R. Bahl , Peter F. Brown , Peter V. DeSouza , Robert L. Mercer
发明人： Lalit R. Bahl , Peter F. Brown , Peter V. DeSouza , Robert L. Mercer
IPC分类号： G10L11/00 , G10L15/06 , G10L15/14
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.

13. 发明授权

US4827521A Training of markov models used in a speech recognition system 失效
标题翻译：在语音识别系统中使用的马尔科夫模型的训练
公开(公告)号：US4827521A
公开(公告)日：1989-05-02
申请号：US845201
申请日：1986-03-27
申请人： Lalit R. Bahl , Peter F. Brown , Peter V. deSouza , Robert L. Mercer
发明人： Lalit R. Bahl , Peter F. Brown , Peter V. deSouza , Robert L. Mercer
IPC分类号： G10L11/00 , G10L15/14 , G10L5/00
CPC分类号： G10L15/14
摘要： In a word, or speech, recognition system for decoding a vocabulary word from outputs selected from an alphabet of outputs in response to a communicated word input wherein each word in the vocabulary is represented by a baseform of at least one probabilistic finite state model and wherein each probabilistic model has transition probability items and output probability items and wherein a value is stored for each of at least some probability items, the present invention relates to apparatus and method for determining probability values for probability items by biassing at least some of the stored values to enhance the likelihood that outputs generated in response to communication of a known word input are produced by the baseform for the known word relative to the respective likelihood of the generated outputs being produced by the baseform for at least one other word. Specifically, the current values of counts --from which probability items are derived--are adjusted by uttering a known word and determining how often probability events occur relative to (a) the model corresponding to the known uttered "correct" word and (b) the model of at least one other "incorrect" word. The current count values are increased based on the event occurrences relating to the correct word and are reduced based on the event occurrences relating to the incorrect word or words.

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