Apparatus and method for performing model estimation utilizing a discriminant measure
First Claim
1. Apparatus for performing acoustic model estimation in order to optimize classification accuracy on feature vectors derived from a speaker with respect to a plurality of classes corresponding to phones to which a plurality of acoustic models respectively correspond, the apparatus comprising:
- means for initializing an acoustic model for each class;
first means for evaluating the merit of the acoustic model initialized for each phone utilizing an objective function having a two component discriminant measure capable of characterizing each phone whereby a first component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from the phone and a second component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from other phones;
means for adapting the acoustic model for selected phones so as to one of increase the first component of the discriminant measure for the phone and decrease the second component of the discriminant measure for the phone, the adapting means yielding a new acoustic model for each selected phone;
second means for evaluating the merit of the new acoustic models for each phone adapted by the adapting means utilizing the two component discriminant measure;
means for comparing results obtained by the first evaluating means with results obtained by the second evaluating means for each phone, and if one of the first component of the discriminant measure has increased and the second component of the discriminant measure has decreased, then the new acoustic model is kept for that phone, else the acoustic model originally initialized is kept;
means for estimating parameters associated with each acoustic model kept for each phone in order to substantially optimize the objective function; and
third means for evaluating termination criterion to determine if the parameters of the acoustic models are substantially optimized.
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Abstract
Method for performing acoustic model estimation to optimize classification accuracy on speaker derived feature vectors with respect to a plurality of classes corresponding to phones to which a plurality of acoustic models respectively correspond comprises: (a) initializing an acoustic model for each phone; (b) evaluating the merit of the acoustic model initialized for each phone utilizing an objective function having a two component discriminant measure capable of characterizing each phone whereby a first component is defined as a probability that the model for the phone assigns to feature vectors from the phone and a second component is defined as a probability that the model for the phone assigns to feature vectors from other phones; (c) adapting the model for selected phones so as to increase the first component for the phone or decrease the second component for the phone, the adapting step yielding a new model for each selected phone; (d) evaluating the merit of the new models for each phone adapted in step (c) utilizing the two component measure; (e) comparing results of the evaluation of step (b) with results of the evaluation of step (d) for each phone, and if the first component has increased or the second component has decreased, the new model is kept for that phone, else the model originally initialized is kept; (f) estimating parameters associated with each model kept for each phone in order to optimize the function; and (g) evaluating termination criterion to determine if the parameters of the models are optimized.
31 Citations
23 Claims
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1. Apparatus for performing acoustic model estimation in order to optimize classification accuracy on feature vectors derived from a speaker with respect to a plurality of classes corresponding to phones to which a plurality of acoustic models respectively correspond, the apparatus comprising:
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means for initializing an acoustic model for each class; first means for evaluating the merit of the acoustic model initialized for each phone utilizing an objective function having a two component discriminant measure capable of characterizing each phone whereby a first component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from the phone and a second component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from other phones; means for adapting the acoustic model for selected phones so as to one of increase the first component of the discriminant measure for the phone and decrease the second component of the discriminant measure for the phone, the adapting means yielding a new acoustic model for each selected phone; second means for evaluating the merit of the new acoustic models for each phone adapted by the adapting means utilizing the two component discriminant measure; means for comparing results obtained by the first evaluating means with results obtained by the second evaluating means for each phone, and if one of the first component of the discriminant measure has increased and the second component of the discriminant measure has decreased, then the new acoustic model is kept for that phone, else the acoustic model originally initialized is kept; means for estimating parameters associated with each acoustic model kept for each phone in order to substantially optimize the objective function; and third means for evaluating termination criterion to determine if the parameters of the acoustic models are substantially optimized. - View Dependent Claims (2, 3, 4)
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5. A method for performing acoustic model estimation in order to optimize classification accuracy on feature vectors derived from a speaker with respect to a plurality of classes corresponding to phones to which a plurality of acoustic models respectively correspond, the method comprising the steps of:
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(a) initializing an acoustic model for each phone; (b) evaluating the merit of the acoustic model initialized for each phone utilizing an objective function having a two component discriminant measure capable of characterizing each phone whereby a first component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from the phone and a second component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from other phones; (c) adapting the acoustic model for selected phones so as to one of increase the first component of the discriminant measure for the phone and decrease the second component of the discriminant measure for the phone, the adapting step yielding a new acoustic model for each selected phone; (d) evaluating the merit of the new acoustic models for each phone adapted in step (c) utilizing the two component discriminant measure; (e) comparing results of the evaluation performed in step (b) with results of the evaluation of step (d) for each phone, and if one of the first component of the discriminant measure has increased and the second component of the discriminant measure has decreased, then the new acoustic model is kept for that phone, else the acoustic model originally initialized is kept; (f) estimating parameters associated with each acoustic model kept for each phone in order to substantially optimize the objective function; and (g) evaluating termination criterion to determine if the parameters of the acoustic models are substantially optimized. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for performing acoustic model estimation in order to optimize classification accuracy on feature vectors derived from a speaker with respect to a plurality of classes corresponding to phones to which a plurality of acoustic models respectively correspond, the method comprising the steps of:
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(a) initializing an acoustic model for each phone; (b) evaluating the merit of the acoustic model initialized for each phone utilizing an objective function having a two component discriminant measure capable of characterizing each phone whereby a first component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from the phone and a second component is defined as a probability that the acoustic model for the phone assigns to the feature vectors from other phones; (c) adapting the acoustic model for selected phones so as to one of increase the first component of the discriminant measure for the phone and decrease the second component of the discriminant measure for the phone, the adapting step yielding a new acoustic model for each selected phone; (d) evaluating the merit of the new acoustic models for each phone adapted in step (c) utilizing the two component discriminant measure; (e) comparing results of the evaluation performed in step (b) with results of the evaluation of step (d) for each phone, and if one of the first component of the discriminant measure has increased and the second component of the discriminant measure has decreased, when the new acoustic model is kept for that phone, else the acoustic model originally initialized is kept; (f) estimating parameters associated with each acoustic model kept for each phone in order to substantially optimize the objective function; and (g) evaluating termination criterion to determine if the parameters of the acoustic models are substantially optimized. - View Dependent Claims (23)
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Specification