CA2216224A1 - Block algorithm for pattern recognition - Google Patents
Block algorithm for pattern recognition Download PDFInfo
- Publication number
- CA2216224A1 CA2216224A1 CA002216224A CA2216224A CA2216224A1 CA 2216224 A1 CA2216224 A1 CA 2216224A1 CA 002216224 A CA002216224 A CA 002216224A CA 2216224 A CA2216224 A CA 2216224A CA 2216224 A1 CA2216224 A1 CA 2216224A1
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- Prior art keywords
- models
- observations
- blocks
- comparing
- subset
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Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/28—Constructional details of speech recognition systems
- G10L15/285—Memory allocation or algorithm optimisation to reduce hardware requirements
Abstract
Comparing a series of observations representing unknown speech, to stored models representing known speech, the series of observations being divided into at least two blocks each comprising two or more of the observations, is carried out in an order which makes better use of memory.
First, the observations in one of the blocks are compared (31), to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models. This step is repeated (33) for models other than those in the subset; and the whole process is repeated (34) for each block.
First, the observations in one of the blocks are compared (31), to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models. This step is repeated (33) for models other than those in the subset; and the whole process is repeated (34) for each block.
Claims (21)
1. A method of comparing a series of observations representing unknown speech, to stored models representing known speech, the series of observations being divided into at least two blocks each comprising two or more of the observations, the method comprising the steps of:
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
2. The method of claim 1 wherein the observations are represented as multidimensional vectors, for the comparison at step a).
3. The method of claim 1 wherein the comparison at step a) uses a Viterbi algorithm.
4. The method of claim 1 wherein the models are represented as finite state machines with probability distribution functions attached.
5. The method of claim 1 wherein the models comprise groups of representations of phonemes.
6. The method of claim 1 wherein the models comprise representations of elements of speech, and step a) comprises the step of:
comparing the block of observations to a predetermined sequence of the models in the subset.
comparing the block of observations to a predetermined sequence of the models in the subset.
7. The method of claim 1 wherein step a) comprises the steps of:
comparing the block of observations to a predetermined sequence of the models in the subset;
determining for each of the models in the sequence, a score which represents the likelihood of a match with the observations compared so far;
storing the score in a score buffer for use in determining scores of subsequent models in the sequence; and determining when the score is no longer needed, then re-using the score buffer to store a subsequent score.
comparing the block of observations to a predetermined sequence of the models in the subset;
determining for each of the models in the sequence, a score which represents the likelihood of a match with the observations compared so far;
storing the score in a score buffer for use in determining scores of subsequent models in the sequence; and determining when the score is no longer needed, then re-using the score buffer to store a subsequent score.
8. The method of claim 1 wherein, step a) comprises the step of:
comparing the block of observations to a lexical graph comprising a predetermined sequence of the models in the subset, wherein the sequence comprises different types of models, and the comparison is dependent on the type; and the method comprises the step of:
determining the types of the models before the block is compared.
comparing the block of observations to a lexical graph comprising a predetermined sequence of the models in the subset, wherein the sequence comprises different types of models, and the comparison is dependent on the type; and the method comprises the step of:
determining the types of the models before the block is compared.
9. The method of claim 1, the models comprising finite state machines, having multiple state sequences, wherein step a) comprises the steps of:
determining state scores for the matches between each respective observation and state sequences of the respective model, making an approximation of the state scores, for the observation, for storing to use in matching subsequent observations, the approximation comprising fewer state scores than were determined for the respective observation.
determining state scores for the matches between each respective observation and state sequences of the respective model, making an approximation of the state scores, for the observation, for storing to use in matching subsequent observations, the approximation comprising fewer state scores than were determined for the respective observation.
10. A method of recognising patterns in a series of observations, by comparing the observations to stored models, using a processing means having a main memory for storing the models and a cache memory, the cache memory being too small to contain all the models and observations, the series of observations being divided into blocks of at least two observations, the method comprising the steps of:
a) using the processor to compare a subset of the models to the observations in one of the blocks of observations, to recognise the patterns, the subset of the models being small enough to fit in the cache memory;
b) repeating step a) for a different subset of the models and;
c) repeating steps a) and b) for a different one of the blocks.
a) using the processor to compare a subset of the models to the observations in one of the blocks of observations, to recognise the patterns, the subset of the models being small enough to fit in the cache memory;
b) repeating step a) for a different subset of the models and;
c) repeating steps a) and b) for a different one of the blocks.
11. A method of recognising patterns in a series of observations by comparing the observations to stored models, the series of observations being divided into at least two blocks each comprising two or more of the observations, the models comprising finite state machines, having multiple state sequences, the method comprising the steps of:
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
12. The method of claim 11 wherein the observations are speech signals, and the models are representations of elements of speech.
13 The method of claim 11 wherein the comparison at step a) uses the Viterbi algorithm.
14. The method of claim 11 wherein the models are represented as finite state machines with probability distribution functions attached.
15. A method of comparing a series of observations representing unknown speech, to stored models representing known speech, by comparing the observations to stored models, the series of observations being grouped into one or more blocks each comprising two or more of the observations, the models comprising finite state machines, having multiple state sequences, the method comprising, for each of the one or more blocks, the steps of:
a) comparing two or more of the observations in the respective block, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match; and b) repeating step a) for models other than those in the subset.
a) comparing two or more of the observations in the respective block, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match; and b) repeating step a) for models other than those in the subset.
16. Software stored on a computer readable medium for comparing a series of observations representing unknown speech, to stored models representing known speech, the series of observations being divided into at least two blocks each comprising two or more of the observations, the software being arranged for carrying out the steps of:
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
17. Software stored on a computer readable medium for recognising patterns in a series of observations by comparing the observations to stored models, the series of observations being divided into at least two blocks each comprising two or more of the observations, the models comprising finite state machines, having multiple state sequences, the software being arranged to carry out the steps of:
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
18. Software stored on a computer readable medium for comparing a series of observations representing unknown speech, to stored models representing known speech, by comparing the observations to stored models, the series of observations being grouped into one or more blocks each comprising two or more of the observations, the models comprising finite state machines, having multiple state sequences, the software being arranged to carry out for each of the one or more blocks, the steps of:
a) comparing two or more of the observations in the respective block, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match; and b) repeating step a) for models other than those in the subset.
a) comparing two or more of the observations in the respective block, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match; and b) repeating step a) for models other than those in the subset.
19. A speech recognition processor for comparing a series of observations representing unknown speech, to stored models representing known speech, the series of observations being divided into at least two blocks each comprising two or more of the observations, the processor being arranged to carry out the steps of:
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
20. A speech recognition processor for recognising patterns in a series of observations by comparing the observations to stored models, the series of observations being divided into at least two blocks each comprising two or more of the observations, the models comprising finite state machines, having multiple state sequences, the processor being arranged to carry out the steps of:
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
a) comparing two or more of the observations in one of the blocks of observations, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match;
b) repeating step a) for models other than those in the subset; and c) repeating steps a) and b) for a different one of the blocks.
21. A speech recognition processor for comparing a series of observations representing unknown speech, to stored models representing known speech, by comparing the observations to stored models, the series of observations being grouped into one or more blocks each comprising two or more of the observations, the models comprising finite state machines, having multiple state sequences, the processor being arranged to carry out, for each of the one or more blocks, the steps of:
a) comparing two or more of the observations in the respective block, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match; and b) repeating step a) for models other than those in the subset.
a) comparing two or more of the observations in the respective block, to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models, by determining which of the state sequences of the respective model is the closest match, and how close is the match; and b) repeating step a) for models other than those in the subset.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002216224A CA2216224A1 (en) | 1997-09-19 | 1997-09-19 | Block algorithm for pattern recognition |
| US09/119,621 US6092045A (en) | 1997-09-19 | 1998-07-21 | Method and apparatus for speech recognition |
| EP98307555A EP0903728A3 (en) | 1997-09-19 | 1998-09-17 | Block algorithm for pattern recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA002216224A CA2216224A1 (en) | 1997-09-19 | 1997-09-19 | Block algorithm for pattern recognition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2216224A1 true CA2216224A1 (en) | 1999-03-19 |
Family
ID=4161510
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002216224A Abandoned CA2216224A1 (en) | 1997-09-19 | 1997-09-19 | Block algorithm for pattern recognition |
Country Status (3)
| Country | Link |
|---|---|
| US (1) | US6092045A (en) |
| EP (1) | EP0903728A3 (en) |
| CA (1) | CA2216224A1 (en) |
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1997
- 1997-09-19 CA CA002216224A patent/CA2216224A1/en not_active Abandoned
-
1998
- 1998-07-21 US US09/119,621 patent/US6092045A/en not_active Expired - Lifetime
- 1998-09-17 EP EP98307555A patent/EP0903728A3/en not_active Withdrawn
Also Published As
| Publication number | Publication date |
|---|---|
| US6092045A (en) | 2000-07-18 |
| EP0903728A2 (en) | 1999-03-24 |
| EP0903728A3 (en) | 2000-01-05 |
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