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The 25-year-old Canadian recalls meeting her husband in the dormitory where she was living while studying at Zhejiang University, and being instantly drawn to him. Millward, who is earning her master's degree, and her husband, Zhang Lie, are in the minority of cross-cultural relationships in a country where it's common to see foreign men dating and marrying Chinese women but not necessarily the other way around.
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Updating automatic electric monophone

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Originally, the performance of such frame matching systems was poor, because the sounds of a given word are rarely, if ever, spoken in exactly the same speed or manner.However, a technique known as Hidden Markov Modeling has been developed which greatly improves the performance of such systems.The senone trees allow all possible triphones to be mapped into a sequence of senones simply by traversing the senone trees associated with the central phoneme of the triphone.As a result, unseen triphones not encountered in the training data can be modeled with senones created using the triphones actually found in the training data.1.

updating automatic electric monophone-52

A major problem in speech recognition is that of reducing the tremendous amount of computation it requires, so that recognition can be performed in a reasonable time on relatively inexpensive computer hardware.Researchers in the art found that improved speech recognition accuracy can be obtained by modeling on a subword level using basic acoustic units known as phonemes.A phoneme is the smallest unit of speech that distinguishes one utterance from another.The storage medium of claim 24 wherein a selected non-leaf node of a selected one of the senone trees for a selected phoneme corresponds to a linguistic question regarding either a phoneme immediately preceding the selected phoneme or a phoneme immediately following the selected phoneme.a plurality of senone trees created using a data set of output distributions based on a set of training words spoken by training users, each training word including one or more phonemes, each phoneme including a predetermined plural number of states, the plurality of senone trees including a separate senone tree for each state of each phoneme of the training words, each senone tree having a plurality of leaf nodes, each leaf node indicating a senone representing one or more output distributions of the data set;computer instructions for causing the computer to detect an unseen triphone in a target word received by the computer, the unseen triphone being a triphone not encountered in one of the training words and including a central phoneme and left and right phonemes positioned immediately adjacent the central phoneme;computer instructions for causing the computer to use the senones obtained for the central phoneme to create a triphone model for the unseen triphone, such that the triphone model can be used for future recognition of spoken words that include the unseen triphone.28.The storage medium of claim 27 wherein each training word has one or more triphones each including a central phoneme and phonemes positioned immediately adjacent the central phoneme, each senone tree having a plurality of nodes including the leaf nodes and non-leaf nodes, the non-leaf nodes including a root node, each non-leaf node corresponding to a linguistic question regarding phoneme context of the phoneme and having branches that correspond to answers to the linguistic question, the senone for each leaf node representing output distributions corresponding to the answers represented by the branches taken from the root node to the leaf node.29.Hidden Markov Modeling determines the probability that a given frame of an utterance corresponds to a given node in an acoustic word model.