Method and apparatus for an adaptive speech recognition system utilizing HMM models
AssigneeSiemens Aktiengesellschaft
InventorsUdo Bub, Harald Höge
Life of this patent
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Abstract
In speech recognition, phonemes of a language are modelled by a hidden Markov model, whereby each status of the hidden Markov model is described by a probability density function. For speech recognition of a modified vocabulary, the probability density function is split into a first and into a second probability density function. As a result thereof, it is possible to compensate variations in the speaking habits of a speaker or to add a new word to the vocabulary of the speech recognition unit and thereby assure that this new word is distinguished with adequate quality from the words already present in the speech recognition unit and is thus recognized.
Claims8
A method for recognizing a predetermined vocabulary in a spoken language with a computer, comprising the steps of:
- (a) determining a digitalized voice signal from the spoken language;
- (b) conducting a signal analysis on the digitalized voice signal to obtain feature vectors for describing the digitalized voice signal;
- (c) conducting a global search for imaging the feature vectors onto a language in model form, wherein each phoneme of the language is described by a modified hidden Markov model and each status of the hidden Markov model is described by a probability density function;
- (d) adapting the probability density function by modifying the vocabulary by splitting the probability density function into a first probability density function and into a second probability density function if a drop of an entropy value is below a predetermined threshold, wherein the adaptation is dynamically performed at run time;
- and
- (e) producing a recognized word sequence based on steps a–d.
Arrangement for recognizing a predetermined vocabulary in a spoken language comprising a processor unit that is configured to:
- (a) determine a digitalized voice signal from the spoken language;
- (b) conduct a signal analysis on the digitalized voice signal, to obtain feature vectors for describing the digitalized voice signal;
- (c) conduct a global search for imaging the feature vectors onto a language present in modeled form, wherein each phoneme of the language is described by a modified hidden Markov model and each status of the hidden Markov model is described by a probability density function;
- (d) adapt a probability density function by modifying the vocabulary, by splitting the probability density function into a first probability density function and into a second probability density function if a drop of an entropy value is below a predetermined threshold, wherein the adaptation is dynamically performed at run time;
- and
- (e) produce a recognized word sequence as a result of steps a–d.
Dependencies are inferred from the claim text.