Method and apparatus for modeling words with multi-arc markov models

Claim 1

A method of modeling a word, said method comprising the steps of: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of at least first and second primitive elemental models of different speech components to form a composite elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; uttering the word one or more times, each utterance of the word producing an observed sequence of component sounds; estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for the first weighting factor from the conditional probability.

Claim 2

A method of modeling a word, said method comprising the steps of: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of at least first and second primitive elemental models of different speech components to form a composite elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; uttering the word one or more times, each utterance of the word producing an observed sequence of component sounds; estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for the first weighting factor from the conditional probability.

Claim 3

A method of modeling a word, said method comprising the steps os: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of at least first and second primitive elemental models of different speech components to form a compositie elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting facotrs; concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; uttering the word one or more times, each utterance of the word producing an observed sequence of component sounds; estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for the first weighting factor from the conditional probability; characterized in that the step of estimating the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds comprises the steps of: estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the probability of occurrence of the composite elemental model given the occurrence of the observed sequence of component sounds; estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the joint probability of occurrence of the first primitive elemental model and the composite elemental model given the occurrence of the observed sequence of component sounds; and estimating the conditional probability as the ratio of the joint probability to the probability of occurrence of the composite elemental model given the observed sequence of component sounds.

Claim 4

A method of modeling a word, said method comprising the steps os: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of at least first and second primitive elemental models of different speech components to form a compositie elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting facotrs; concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; uttering the word one or more times, each utterance of the word producing an observed sequence of component sounds; estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for the first weighting factor from the conditional probability; characterized in that the step of estimating the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of component sounds comprises the steps of: estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the probability of occurrence of the composite elemental model given the occurrence of the observed sequence of component sounds; estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the joint probability of occurrence of the first primitive elemental model and the composite elemental model given the occurrence of the observed sequence of component sounds; and estimating the conditional probability as the ratio of the joint probability to the probability of occurrence of the composite elemental model given the observed sequence of component sounds.

Claim 10

An apparatus for modeling a word, said apparatus comprising: means for storing a finite set of n primitive elemental models, where n is an integer greater than or equal to two, each primitive elemental model representing a speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; means for combining the first states of at least first and second primitive elemental models of different speech components to form a composite elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; means for concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; means for measuring the value of at least one feature of one or more utterances of the word, each utterance occurring over a series of successive time intervals, said means measuring the feature value of the utterance during each time interval to produce a sequence of observed acoustic vector signals representing the feature values; means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of acoustic vector signals; and means for estimating a posterior value for the first weighting factor from the conditional probability.

Claim 11

An apparatus for modeling a word, said apparatus comprising: means for storing a finite set of n primitive elemental models, where n is an integer greater than or equal to two, each primitive elemental model representing a speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; means for combining the first states of at least first and second primitive elemental models of different speech components to form a composite elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; means for concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; means for measuring the value of at least one feature of one or more utterances of the word, each utterance occurring over a series of successive time intervals, said means measuring the feature value of the utterance during each time interval to produce a sequence of observed acoustic vector signals representing the feature values; means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of acoustic vector signals; and means for estimating a posterior value for the first weighting factor from the conditional probability.

Claim 12

An apparatus for modeling a word, said apparatus comprising: means for storing a finite set of n primitive elemental models, where n is an integer greater than or equal to two, each primitive elemental model representing a speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; means for combining the first states of at least first and second primitive elemental models of different speech components to form a composite elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; means for concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; means for measuring the value of at least one feature of one or more utterances of the word, each utterance occurring over a series of successive time intervals, said means measuring the feature value of the utterance during each time interval to produce a sequence of observed acoustic vector signals representing the feature values; means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of acoustic vector signals; and means for estimating a posterior value for the first weighting factor from the conditional probability; characterized in that the means for estimating the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the ocurrence of the observed sequence of acoustic vector signals comprises: means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the probability of occurrence of the composite elemental model given the occurrence of the observed sequence of acoustic vectors signals; means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the joint probability of occurrence of the first primitive elemental model and the composite elemental model given the occurrence of the observed sequence of acoustic vector signals; and means for estimating the conditional probability as the ratio of the joint probability to the probability of occurrence of the composited elemental model given the observed sequence of acoustic vector signals.

Claim 13

An apparatus for modeling a word, said apparatus comprising: means for storing a finite set of n primitive elemental models, where n is an integer greater than or equal to two, each primitive elemental model representing a speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; means for combining the first states of at least first and second primitive elemental models of different speech components to form a composite elemental model having at least first and second weighting factors, respectively, each weighting factor having a prior value, said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; means for concatenating a series of elemental models to form a word model, at least one elemental model in the series being the composite elemental model; means for measuring the value of at least one feature of one or more utterances of the word, each utterance occurring over a series of successive time intervals, said means measuring the feature value of the utterance during each time interval to produce a sequence of observed acoustic vector signals representing the feature values; means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the occurrence of the observed sequence of acoustic vector signals; and means for estimating a posterior value for the first weighting factor from the conditional probability; characterized in that the means for estimating the conditional probability of occurrence of the first primitive elemental model given the occurrence of the composite elemental model and given the ocurrence of the observed sequence of acoustic vector signals comprises: means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the probability of occurrence of the composite elemental model given the occurrence of the observed sequence of acoustic vectors signals; means for estimating, from the prior values of the first and second weighting factors and from the values of the parameters of the first and second primitive elemental models, the joint probability of occurrence of the first primitive elemental model and the composite elemental model given the occurrence of the observed sequence of acoustic vector signals; and means for estimating the conditional probability as the ratio of the joint probability to the probability of occurrence of the composited elemental model given the observed sequence of acoustic vector signals.

Claim 17

A method of modeling a word, said method comprising the steps of: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of all n primitive elemental models to form a set of composite elemental models, each composite elemental model having n weighting factors W n for the n primitive elemental models, respectively, each weighting factor having a prior value, for each composite elemental model said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; concatenating a series of composite elemental models to form a word model; uttering the word one or more times, each utterance of the word producing an observed sequence of component sounds; estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the conditional probability of occurrence of each primitive elemental model given the occurrence of each composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for each weighting factor from the conditional probabilities.

Claim 18

A method of modeling a word, said method comprising the steps of: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of all n primitive elemental models to form a set of composite elemental models, each composite elemental model having n weighting factors W n for the n primitive elemental models, respectively, each weighting factor having a prior value, for each composite elemental model said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; concatenating a series of composite elemental models to form a word model; uttering the word one or more times, each utterance of the word producing an observed sequence of component sounds; estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the conditional probability of occurrence of each primitive elemental model given the occurrence of each composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for each weighting factor from the conditional probabilities.

Claim 19

A method of modeling a word, said method comprising the steps of: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of all n primitive elemental models to form a set of composite elemental models, each composite elemental model having n weighting factors W n for the n primitive elemental models, respectively, each weighting factor having a prior value, for each composite elemental model said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; concatenating a series of composite elemental models to form a word model; uttering the word one or more times, each utterance of the word producing an observed sequence component sounds; estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the conditional probability of occurrence of each primitive elemental model given the occurrence of each composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for each weighting factor from the conditional probabilities; characterized in that the step of estimating the conditional probability of occurrence of a primitive elemental model given the occurrence of a composite elemental model and given the occurrence of the observed sequence of component sounds comprises the steps of: estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the probability of occurrence of the composite elemental model given the occurrence of the observed sequence of component sounds; estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the joint probability of occurrence of the primitive elemental model and the composite elemental model given the occurrence of the observed sequence of component sounds; and estimating the conditional probability as the ratio of the joint probability to the probability of occurrence of the composite elemental model given the observed sequence of component sounds.

Claim 20

A method of modeling a word, said method comprising the steps of: defining a finite set of n speech components, where n is an integer greater than or equal to two; providing a primitive elemental model for each speech component, each primitive elemental model having at least first and second states, at least one transition from the first state to the second state, and at least one parameter having a value; combining the first states of all n primitive elemental models to form a set of composite elemental models, each composite elemental model having n weighting factors W n for the n primitive elemental models, respectively, each weighting factor having a prior value, for each composite elemental model said primitive elemental models being combined by a weighted combination of their parameters in proportion to the values of the weighting factors; concatenating a series of composite elemental models to form a word model; uttering the word one or more times, each utterance of the word producing an observed sequence component sounds; estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the conditional probability of occurrence of each primitive elemental model given the occurrence of each composite elemental model and given the occurrence of the observed sequence of component sounds; and estimating a posterior value for each weighting factor from the conditional probabilities; characterized in that the step of estimating the conditional probability of occurrence of a primitive elemental model given the occurrence of a composite elemental model and given the occurrence of the observed sequence of component sounds comprises the steps of: estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the probability of occurrence of the composite elemental model given the occurrence of the observed sequence of component sounds; estimating, from the prior values of the weighting factors and from the values of the parameters of the primitive elemental models, the joint probability of occurrence of the primitive elemental model and the composite elemental model given the occurrence of the observed sequence of component sounds; and estimating the conditional probability as the ratio of the joint probability to the probability of occurrence of the composite elemental model given the observed sequence of component sounds.