Method and apparatus for probabilistic recognition using small number of state clusters
AssigneeSRI International
InventorsAnanth Sankar, Venkata Ramana Rao Gadde
Life of this patent
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Abstract
Probabilistic recognition using clusters and simple probability functions provides improved performance by employing a limited number of clusters each using a relatively large number of simple probability functions. The simple probability functions for each of the limited number of state clusters are greater in number than the limited number of state clusters.
Claims18
In a speech recognition system using a method for recognizing human speech, the method comprising the steps of:
- selecting a model to represent a selected subunit of speech, the model having associated with it a plurality of states;
- determining states that may be represented by a set of simple probability functions;
- and
- clustering said states that may be represented by a set of simple probability functions into a limited number of clusters, wherein said simple probability functions for each of said limited number of state clusters is greater in number than said limited number of state clusters.
A computer readable medium having stored thereon a plurality of instructions, the plurality of instructions including instructions which, when executed by a processor, cause the processor to perform the steps comprising of:
- selecting a model to represent a selected subunit of speech, the model having associated with it a plurality of states;
- determining states that may be represented by a set of simple probability functions;
- and
- clustering said states that may be represented by a set of simple probability functions into a limited number of clusters, wherein said simple probability functions for each of said limited number of state clusters is greater in number than said limited number of state dusters.
A speech recognizer comprising:
- a logic processing device;
- storage means;
- a set of probabilistic models stored in the storage means;
- said models including a limited number of state clusters, wherein at least one of said limited number of state clusters is represented by a number of simple probability functions, wherein said simple probability functions for each of said limited number of state clusters is greater in number than said limited number of state clusters;
- a feature extractor in a computer for extracting feature data capable of being processed by said computer from a speech signal;
- and
- recognizing means for matching features from unidentified speech data to the models to produce a most likely path through the models where the path defines the most likely subunits and words in the speech data.
Dependencies are inferred from the claim text.