Abstract: A sequencer device generates basic nucleotide sequence data 30 comprising probe data 34 of a capture probe in the sequencer device 10 and a determined sequence of identifiers 32 of a fragment of nucleic acids captured by the probe. The sequencer device outputs enriched nucleotide sequence data 36 that is enriched with data comprising a reference to a sequence 38 that is expected for the fragment of nucleic acids.

Abstract: Methods and apparatus for identifying a user group in connection with user group-based speech recognition. An exemplary method comprises receiving, from a user, a user group identifier that identifies a user group to which the user was previously assigned based on training data. The user group comprises a plurality of individuals including the user. The method further comprises using the user group identifier, identifying a pattern processing data set corresponding to the user group, and receiving speech input from the user to be recognized using the pattern processing data set.

Abstract: Methods and apparatus for identifying a user group in connection with user group-based speech recognition. An exemplary method comprises receiving, from a user, a user group identifier that identifies a user group to which the user was previously assigned based on training data. The user group comprises a plurality of individuals including the user. The method further comprises using the user group identifier, identifying a pattern processing data set corresponding to the user group, and receiving speech input from the user to be recognized using the pattern processing data set.

Abstract: Methods and apparatus for identifying a user group in connection with user group-based speech recognition. An exemplary method comprises receiving, from a user, a user group identifier that identifies a user group to which the user was previously assigned based on training data. The user group comprises a plurality of individuals including the user. The method further comprises using the user group identifier, identifying a pattern processing data set corresponding to the user group, and receiving speech input from the user to be recognized using the pattern processing data set.

Abstract: A sequencer device generates basic nucleotide sequence data 30 comprising probe data 34 of a capture probe in the sequencer device 10 and a determined sequence of identifiers 32 of a fragment of nucleic acids captured by the probe. The sequencer device outputs enriched nucleotide sequence data 36 that is enriched with data comprising a reference to a sequence 38 that is expected for the fragment of nucleic acids.

Abstract: Methods and apparatus for identifying a user group in connection with user group-based speech recognition. An exemplary method comprises receiving, from a user, a user group identifier that identifies a user group to which the user was previously assigned based on training data. The user group comprises a plurality of individuals including the user. The method further comprises using the user group identifier, identifying a pattern processing data set corresponding to the user group, and receiving speech input from the user to be recognized using the pattern processing data set.

Abstract: A method for recognizing a pattern that comprises a set of physical stimuli, said method comprising the steps of: providing a set of training observations and through applying a plurality of association models ascertaining various measuring values pj(k|x), j=1 . . . M, that each pertain to assigning a particular training observation to one or more associated pattern classes; setting up a log/linear association distribution by combining all association models of the plurality according to respective weight factors, and joining thereto a normalization quantity to produce a compound association distribution; optimizing said weight factors for thereby minimizing a detected error rate of the actual assigning to said compound distribution; recognizing target observations representing a target pattern with the help of said compound distribution.

Abstract: The invention relates to a method of pattern processing, in particular for speech processing, comprising the steps of:

Abstract: The invention relates to a method of training parameters of a pattern recognition system, each parameter being associated with exactly one realization variant of a pattern from an inventory, comprising the steps of:

Abstract: A method for recognizing a pattern that comprises a set of physical stimuli, said method comprising the steps of:

Abstract: A method for recognizing a pattern that comprises a set of physical stimuli, said method comprising the steps of: providing a set of training observations and through applying a plurality of association models ascertaining various measuring values pj(k|x), j=1 . . . M, that each pertain to assigning a particular training observation to one or more associated pattern classes; setting up a log/linear association distribution by combining all association models of the plurality according to respective weight factors, and joining thereto a normalization quantity to produce a compound association distribution; optimizing said weight factors for thereby minimizing a detected error rate of the actual assigning to said compound distribution; recognizing target observations representing a target pattern with the help of said compound distribution.

Abstract: For the recognition of spoken text it is necessary that the words to be recognized are available in acoustically modeled form, i.e. in the form of a sequence of reference values. These reference values are determined from a known, spoken text during a training phase, in that from this text there are derived characteristic values at regular intervals, as during the recognition, which characteristic values are arranged according to triphones so as to form groups or so-called clusters. These groups constitute the basis for the reference values. In the case of a recognition system involving a very large vocabulary, however, not all triphones will occur during the training phase, unless the text is prohibitively long. In order to enable the reference values to be determined also for words containing triphones which have not occurred, such a triphone must be associated with an available group.

Abstract: A system and method are used for recognising a time-sequential input pattern (20), which is derived from a continual physical quantity, such as speech. The system has input means (30), which accesses the physical quantity and therefrom generates a plurality of input observation vectors. The input observation vectors represent the input pattern. A reference pattern database (40) is used for storing a plurality of reference patterns. Each reference pattern includes a sequence of reference units, where each reference unit is represented by at least one associated reference vector .mu..sub.a in a set {.mu..sub.a } of reference vectors. A localizer (50) is used for locating among the reference patterns stored in the reference pattern database (40), a recognised reference pattern, which corresponds to the input pattern. The locating includes selecting a subset {.mu..sub.s } of reference vectors from said set {.mu..sub.

Abstract: For speech recognition a new word is represented as based on a stored inventory of models of sub-word units. First a plurality of utterances are presented that all should conform to the word. For building a word model from the utterances, these are represented by a sequence of feature vectors. First, the utterances are used to train a whole-word model that is independent of the models of the sub-word units. The length of the whole-word model equals the average length of the utterances. Next, a sequence of Markov states and associated probability densities of acoustic events of the whole-word model is interpreted as a reference template represented by a string of averaged feature vectors. Finally, the string is recognized by matching to models in the inventory and storing a recognition result as a model of the utterances.