US5129002A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Pattern recognition apparatus
Assignee: Panasonic Holdings Corp
•
Filing: 1991-11-05
•
Granted: 1992-07-07
•
Expires: 2011-11-05
Abstract
Abstract A pattern recognition apparatus using the hidden Markov model technique in which parameters for defining a mean vector representing the probability density function in each one of plural states for composing the hidden Markov model vary with time. Accordingly, the recognition precision may be enhanced when this apparatus is used in, for example, voice recognition.
Inventors
- 👤 Eiichi Tsuboka
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-28 03:49
CPC Classifications
G; G10; G10L; G10L15/00; G10L15/08; G10L15/14; G10L15/142; G10L15/144
Claims Analysis
Total Claims: 21
Claim 1
Independent Claim
100 words
A pattern recognition signal analysis apparatus for generating and storing an improved Hidden Markox Model (HMM), comprising: feature extraction means for converting an input signal into a sequence of feature vectors, one for each state; HMM creating means for making an HMM from said sequence of extracted feature vectors by calculating parameters for each said feature vector on the basis of previously-defined parameters to provide a mean vector representing the probability density function of each feature vector for each state, each said means vector varying with time within each state; and means employing said HMM for recognizing an unknown pattern.
Claim 2
Dependent Claim
100 words
A pattern recognition signal analysis apparatus for generating and storing an improved Hidden Markox Model (HMM), comprising: feature extraction means for converting an input signal into a sequence of feature vectors, one for each state; HMM creating means for making an HMM from said sequence of extracted feature vectors by calculating parameters for each said feature vector on the basis of previously-defined parameters to provide a mean vector representing the probability density function of each feature vector for each state, each said means vector varying with time within each state; and means employing said HMM for recognizing an unknown pattern.
Claim 3
Dependent Claim
46 words
The pattern recognition apparatus of claim 1, wherein said parameters for defining each said mean vector for a state (i) comprise: a neutral point vector μ i , a direction vector u i , transition probability γ i , and a covariance matrix Σ i .
Claim 4
Dependent Claim
20 words
The pattern recognition apparatus of claim 2 further comprising: means employing the created HMM for recognizing an unknown input signal.
Claim 5
Dependent Claim
27 words
The pattern recognition apparatus of claim 2 wherein said HMM creating means calculates said parameters from a training pattern set made of a plurality oflike training patterns.
Claim 6
Dependent Claim
67 words
The pattern recognition apparatus of claim 4 wherein said measn for recognizing an unknown input signal comprises: means for converting said unknown input signal into an observed sequence of feature vectors, one for each state; and means for calculating an observed mean vector representing the probability density function for each said feature vector for each state, each said observed mean vector varying with time within each state.
Claim 7
Dependent Claim
38 words
The pattern recognition apparatus of claim 5 wherein said means for recognizing further comprises: means for calculating the likelihood in terms of a probability density function that said observed mean vector was generated by said HMM creating means.
Claim 8
Dependent Claim
127 words
A voice recognition apparatus using a plurality of Hidden Markov Models (HMM), each having a plurality of states, comprising: feature extraction means for converting an input signal into a sequence of feature vectors; HMM creating means for making a plurality of HMMs from the sequence of feature vectors, said HMM creating means utilizing at least one parameter for defining a mean vector in termsofa probability density function of the feature vector in each state, said mean vector varying with time in each state; means for storing said plurality of HMMs; and means connected to said storing means for receiving a series of unknown utterances and for employing the HMMs stored by said means for storing to determine the utterance most likely to correspond to said unknown utterance.
Claim 9
Dependent Claim
127 words
A voice recognition apparatus using a plurality of Hidden Markov Models (HMM), each having a plurality of states, comprising: feature extraction means for converting an input signal into a sequence of feature vectors; HMM creating means for making a plurality of HMMs from the sequence of feature vectors, said HMM creating means utilizing at least one parameter for defining a mean vector in termsofa probability density function of the feature vector in each state, said mean vector varying with time in each state; means for storing said plurality of HMMs; and means connected to said storing means for receiving a series of unknown utterances and for employing the HMMs stored by said means for storing to determine the utterance most likely to correspond to said unknown utterance.
Claim 10
Independent Claim
146 words
A method of speech pattern recognition employing a Hidden Markox Model (HMM), said model comprising a plurality of states, the steps comprising: uttering a test word W for R times where R is an integer; converting each utterance of the word W into a sequence of feature vectors; storing each sequence of feature vectors in a memory; determining the elements of a Markov model parameter set λ i utilizing said feature vector sequences, said step of determining including the determination of a neutral pint vector anda direction vector, the point vector and direction vector being related to a mean vector of a probability density function, said mean vector varying linearly with time for each state of said model; storing said Markov model parameter set; and employing the stored Markov model parameter set in selecting a speech pattern most likely to correspond to an unknown speech pattern.
Claim 11
Independent Claim
146 words
A method of speech pattern recognition employing a Hidden Markox Model (HMM), said model comprising a plurality of states, the steps comprising: uttering a test word W for R times where R is an integer; converting each utterance of the word W into a sequence of feature vectors; storing each sequence of feature vectors in a memory; determining the elements of a Markov model parameter set λ i utilizing said feature vector sequences, said step of determining including the determination of a neutral pint vector anda direction vector, the point vector and direction vector being related to a mean vector of a probability density function, said mean vector varying linearly with time for each state of said model; storing said Markov model parameter set; and employing the stored Markov model parameter set in selecting a speech pattern most likely to correspond to an unknown speech pattern.
Claim 12
Dependent Claim
89 words
A pattern recognition apparatus using a plurality of Hidden Markov Models (HMM) each having a plurality of states, comprising: feature extraction means for converting an input signal into a sequence of feature vectors; and HMM creating means for making an HMM from the sequence of feature vectors utilizing at least one parameter for defining a probability density function of the feature vector in each state, the probability density function comprising a mean vector varying with time in each state; and means employing said HMM for recognizing an unknown pattern.
Claim 13
Dependent Claim
89 words
A pattern recognition apparatus using a plurality of Hidden Markov Models (HMM) each having a plurality of states, comprising: feature extraction means for converting an input signal into a sequence of feature vectors; and HMM creating means for making an HMM from the sequence of feature vectors utilizing at least one parameter for defining a probability density function of the feature vector in each state, the probability density function comprising a mean vector varying with time in each state; and means employing said HMM for recognizing an unknown pattern.
Claim 14
Dependent Claim
18 words
The apparatus of claim 9 wherein said mean vector varies linearly with respect to time in each state.
Claim 15
Dependent Claim
26 words
The apparatus of claim 10 wherein said probability density function further includes a term comprising a covariance matrix which is constant in time in each state.
Claim 16
Dependent Claim
18 words
The apparatus of claim 10 wherein said mean vector comprises a neutral point vector and a direction vector.
Claim 17
Dependent Claim
30 words
The apparatus of claim 9 wherein, in the course of making an HMM, said HMM creating means estimates a mean vector, said mean vector varying with time in each state.
Claim 18
Dependent Claim
28 words
The apparatus of claim 13 wherein, in the course of making an HMM, said HMM creating means further estimates a covariance matrix which is constant in each state.
Claim 19
Dependent Claim
27 words
The apparatus of claim 14 wherein said HMM creating means estimates said mean vector by deriving an estimate of a neutral point vector and a direction vector.
Claim 20
Dependent Claim
119 words
In a method of deriving a Hidden Markov Models (HMM) for speech pattern recognition, said model comprising a plurality of states, the steps comprising: uttering a word W for R times where R is an integer; converting each utterance of the word W into a sequence of feature vectors; storing each sequence of feature vectors in a memory; and calculating a Markov model parameter set λ i utilizing said feature vector sequences, said step of calculating including the determination of a neutral point vector and a direction vector, the neutral point vector and direction vector being related to a mean vector of a probability density function, said mean vector varying linearly with time for each state of said model.
Claim 21
Dependent Claim
119 words
In a method of deriving a Hidden Markov Models (HMM) for speech pattern recognition, said model comprising a plurality of states, the steps comprising: uttering a word W for R times where R is an integer; converting each utterance of the word W into a sequence of feature vectors; storing each sequence of feature vectors in a memory; and calculating a Markov model parameter set λ i utilizing said feature vector sequences, said step of calculating including the determination of a neutral point vector and a direction vector, the neutral point vector and direction vector being related to a mean vector of a probability density function, said mean vector varying linearly with time for each state of said model.
References Cited (Backward Citations)
US4790017A
Cited
US4780906A
Cited
US4783802A
Cited
US4783809A
Cited
US4783804A
Cited
US4783806A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1995-07-07 | 1996-01-06 | 1996-07-07 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 1999-07-07 | 2000-01-06 | 2000-07-07 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2003-07-07 | 2004-01-06 | 2004-07-07 | $7700.00 | Window Closed / Lapsed |