Koninklijke Philips Corporation N.v.

U.S. Philips Corporation

Stefan Besling

Eric Thelen

Meinhard Ullrich

Steven DeJarnett

Hans-Gunter Meier

In a method in which an information unit ( ) enabling a speech input is stored on a server ( ) and can be retrieved by a client ( ) and in which the client can be coupled to one or more speech recognizers ( ) through a communications network ( ), the information unit ( ) is assigned additional information ( ) which is provided for determining a combination of a client ( ) for recognizing an uttered speech signal and at least one of the speech recognizers ( ), to dynamically assign the speech recognizers ( ) in a communications network ( ) to the information units ( ) and thus ensure an acceptable processing time for the recognition of a speech input with a high recognition quality.

Method of speech recognition

A huge vocabulary speech recognition system for recognizing a sequence of spoken words, having an input means for receiving a time-sequential input pattern representative of the sequence of spoken words. The system further includes a plurality of large vocabulary speech recognizers each being associated with a respective, different large vocabulary recognition model. Each of the recognition models is targeted to a specific part of the huge vocabulary. The system comprises a controller operative to direct the input pattern to a plurality of the speech recognizers and to select a recognized word sequence from the word sequences recognized by the plurality of speech recognizers.

Speech recognition system having parallel large vocabulary recognition engines

A system includes means for creating a vocabulary and/or statistical language model from a textual training corpus. The vocabulary and/or language model are used in a pattern recognition system, such as a speech recognition system or a handwriting recognition system, for recognizing a time-sequential input pattern. The system includes means for determining at least one context identifier and means for deriving at least one search criterion, such as a keyword, from the context identifier. The system further includes means for selecting documents from a set of documents based on the search criterion. Advantageously, an Internet search engine is used for selecting the documents. Means are used for composing the training corpus from the selected documents.

Vocabulary and/or language model training

A distributed pattern recognition system includes at least one user station and a server station. The server station and the user station are connected via a network, such as Internet. The server station includes different recognition models of a same type. As part of a recognition enrolment, the user station transfers model improvement data associated with a user of the user station to the server station. The server station selects a recognition model from the different recognition models of a same type in dependence on the model improvement data. For each recognition session, the user station transfers an input pattern representative of time sequential input generated by the user to the server station. The server station retrieves a recognition model selected for the user and provides the retrieved recognition model to a recognition unit for recognising the input pattern using the recognition models.

User model-improvement-data-driven selection and update of user-oriented recognition model of a given type for word recognition at network server

For speech recognition, notably the recognition of curtly spoken speech with a large vocabulary, language models which take into account the probabilities of occurrence of word sequences are used so as to enhance the recognition reliability. These language models are determined from rather large quantities of text and hence represent an average value formed over several texts. However, the language model hence is not adapted very well to particularities of a special text. In order to achieve such adaptation of an existing language model to a special text while using only a small quantity of text, the invention proposes to determine confidence intervals from the counts of the word sequences occurring in the short text; this determination is possible by using calculation methods which are known from statistics. Subsequently, for each predecessor word sequence there is determined a scaling factor which adapts the language model values for all words in such a manner that as many adapted language model values as possible are situated within the confidence intervals. If scaled language model values are situated outside associated confidence intervals after the adaptation, the nearest boundaries of the confidence intervals are used as adapted language model values.

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Aachen, Germany

Stefan Besling