System and Method for Recognizing Speech Securely
First Claim
1. A system for recognizing speech securely, comprising:
- a client configured to provide securely speech in a form of an observation sequence of symbols;
a server configured to provide securely a plurality of trained hidden Markov models (HMMs), each trained HMM including a plurality of states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution, and in which the observation symbol probability distributions are modeled by mixtures of Gaussian distributions;
means to determine securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM; and
means to determine securely a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech,
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Abstract
A system and method recognizes speech securely. The system includes a client and a server, The client is configured to provide securely speech in a form of an observation sequence of symbols, and the server is configured to provide securely a multiple trained hidden Markov models (HMMs), each trained HMM including a multiple states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution. The observation symbol probability distributions are modeled by mixtures of Gaussian distributions. Also included are means for determining securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM, and means for determining a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech.
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Citations
10 Claims
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1. A system for recognizing speech securely, comprising:
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a client configured to provide securely speech in a form of an observation sequence of symbols; a server configured to provide securely a plurality of trained hidden Markov models (HMMs), each trained HMM including a plurality of states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution, and in which the observation symbol probability distributions are modeled by mixtures of Gaussian distributions; means to determine securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM; and means to determine securely a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech, - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for recognizing speech securely, comprising:
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providing securely speech in a form of an observation sequence of symbols; providing securely a plurality of trained hidden Markov models (HMMs), each trained HMM including a plurality of states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution, and in which the observation symbol probability distributions are modeled by mixtures of Gaussian distributions; determining securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM; and determining securely a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech.
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Specification