International Business Machines Corporation

Apple Inc.

Peter Vincent deSouza

Lalit R Bahl

Edward Adam Epstein

Burn Lewin Lewis

Steven V De Gennaro

Jean-Michel Le Roux

Claire Waast-Richard

David Nahamoo

Hsiao-Wuen Hon

Mukund Padmanabhan

Yen-Lu Chow

Adam B Fineberg

A system and method are provided which partition the feature space of a classifier by using hyperplanes to construct a binary decision tree or hierarchical data structure for obtaining the class probabilities for a particular feature vector. One objective in the construction of the decision tree is to minimize the average entropy of the empirical class distributions at each successive node or subset, such that the average entropy of the class distributions at the terminal nodes is minimized. First, a linear discriminant vector is computed that maximally separates the classes at any particular node. A threshold is then chosen that can be applied on the value of the projection onto the hyperplane such that all feature vectors that have a projection onto the hyperplane that is less than the threshold are assigned to a child node (say, left child node) and the feature vectors that have a projection greater than or equal to the threshold are assigned to a right child node. The above two steps are then repeated for each child node until the data at a node falls below a predetermined threshold and the node is classified as a terminal node (leaf of the decision tree). After all non-terminal nodes have been processed, the final step is to store a class distribution associated with each terminal node. The class probabilities for a particular feature vector can then be obtained by traversing the decision tree in a top-down fashion until a terminal node is identified which corresponds to the particular feature vector. The information provided by the decision tree is that, in computing the class probabilities for the particular feature vector, only the small number of classes associated with that particular terminal node need be considered. Alternatively, the required class probabilities can be obtained simply by taking the stored distribution of the terminal node associated with the particular feature vector.

System and method for partitioning the feature space of a classifier in

A system and method for recognizing spoken liaisoned words. The method and system identify each word in the vocabulary as a liaison generator and/or liaison receptor. If the word is a liaison receptor, and if the word is preceded by a liaison generator, the most probable recognition result for the word will be the liaison generated by the preceding word plus the word. Liaisons are identified on an immediately preceding word in accordance with rules in a language. A word that ends with an unpronounced consonant phoneme, when followed by a word beginning with a consonant phoneme, and ends with a pronounced phoneme, when followed by a word with a vowel-like phoneme, causes a match list for the current word to be amended with words having liaisons added at their beginnings.

Method for modeling and recognizing speech including word liaisons

A method and system of recognizing speech. The method and system perform a fast match on a word in the string of speech to be recognized which generates a fast match list representing words in a system vocabulary that most likely match a current word to be recognized. Next, the method and system perform a detailed match on the words in the fast match list and generate a detailed match list representing words that most likely match the current word to be recognized. Then for each word in the detailed match list that can accept a liaison phoneme from a preceding word, where each word is a liaison receptor, adding to the detailed match list a form of the liaison receptor, where the form represents an addition of a liaison phoneme to the liaison receptor, creating a modified detailed match list which is inclusive of the forms of the liaison receptors added to the detailed match list. Finally the method and system outputs a word in the modified detailed match list that has the highest probability of matching the word to be recognized.

Recognizing speech having word liaisons by adding a phoneme to reference

A pattern recognition system which continuously adapts reference patterns to more effectively recognize input data from a given source. The input data is converted to a set or series of observed vectors and is compared to a set of Markov Models. The closest matching Model is determined and is recognized as being the input data. Reference vectors which are associated with the selected Model are compared to the observed vectors and updated ('adapted') to better represent or match the observed vectors. This updating method retains the value of these observed vectors in a set of accumulation vectors in order to base future adaptations on a broader data set. When updating, the system also may factor in the values corresponding to neighboring reference vectors that are acoustically similar if the data set from the single reference vector is insufficient for an accurate calculation. Every reference vector is updated after every input; thus reference vectors neighboring an updated reference vector may also be updated. The updated reference vectors are then stored by the computer system for use in recognizing subsequent inputs.

Continuous reference adaptation in a pattern recognition system

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San Jose, CA, United States of America

Peter Vincent deSouza