Predicting auditory confusions using a weighted Levinstein distance

Claim 3

The method of claim 1, wherein the step of obtaining the phonemic transformation weights includes the substep of determining the phonemic transformation weight based on phonological features of phonemes used for edit operations.

Claim 4

The method of claim 2, wherein the substep of determining the phonemic weight comprises using the following equation: ##EQU5## where N represents the number of phonological feature types for each phoneme, α i is the relative significance of feature type i, functions f i (/x/) and f i (/y/) represent feature value of the feature type i of phoneme /x/ and /y/, and t(f(/x/), f(/y/)) represents a transformation cost between feature values f(/x/) and f(/y/).

Claim 5

The method of claim 1, wherein the step of obtaining the phonemic transformation weights includes the substep of determining the phonemic transformation weight of a transformation between two phonemes based on Hidden Markov Models of the phonemes.

Claim 6

The method of claim 4, wherein the phonemic transformation weights obtained in the substep of determining the phonemic transformation weight of the transformation between two phonemes is calculated using the equation: ##EQU6## where n represents the number of states used to model a phoneme, v i is a heuristically set weight for each said state used to model the phoneme, and H(x i , y i ) represents relative entropy between probability mixtures of a state I of phoneme x and a state I of phoneme y.

Claim 7

The method of claim 1, wherein the step of summing the obtained phonemic transformation weights further includes the step of normalizing the value indicating the likelihood of confusion between the first and second words.

Claim 8

The method of claim 1, wherein the step of calculating the Levinstein distance includes the substep of calculating multiple Levinstein distances between the first and second phonemic transcriptions.

Claim 9

The method of claim 1, further including the step of modifying a lexicon based on a generated value to minimize potential confusability between words in the lexicon.

Claim 10

The method of claim 1, further including the step of using a generated value as part of a speech recognition algorithm to translate a spoken word into a computer representation of the spoken word.

Claim 11

The method of claim 1, further including the step of using a generated values to build test lexicons to be used in evaluating speech recognition algorithms.

Claim 12

A computer readable medium containing instructions executable on a computer for determining the likelihood of confusing first and second phonemic transcriptions, the instructions causing the computer to: calculate a Levinstein distance between the first and second phonemic transcriptions, wherein the Levinstein distance corresponds to edit operations required to transform the first phonemic transcription into the second phonemic transcription; obtain a phonemic transformation weight for each edit operation of the Levinstein distance; and sum the obtained phonemic transformation weights to generate a value indicating the likelihood of confusion between the first and second phonemic transcriptions, that correspond to spoken words.

Claim 13

A computer readable medium containing instructions executable on a computer for determining the likelihood of confusing first and second phonemic transcriptions, the instructions causing the computer to: calculate a Levinstein distance between the first and second phonemic transcriptions, wherein the Levinstein distance corresponds to edit operations required to transform the first phonemic transcription into the second phonemic transcription; obtain a phonemic transformation weight for each edit operation of the Levinstein distance; and sum the obtained phonemic transformation weights to generate a value indicating the likelihood of confusion between the first and second phonemic transcriptions, that correspond to spoken words.

Claim 14

The computer readable medium of claim 11, further including instructions causing the computer to determine the phonemic transformation weight for each edit operation of the Levinstein distance based on phonological features of phonemes used for the first and second phonemic transcription.

Claim 15

The computer readable medium of claim 12, further including instructions causing the computer to determine the phonemic transformation weight as: ##EQU7## where N represents the number of phonological feature types for each phoneme, α i is the relative significance of feature type i, functions f i (/x/) and f i (/y/) represent feature value of the feature type i of phoneme /x/ and /y/, and t(f(/x/), f(/y/)) represents a transformation cost between feature values f(/x/) and f(/y/).

Claim 16

The computer readable medium of claim 11, further including instructions causing the computer to determine the phonemic transformation weight between two phonemes based on Hidden Markov Models of the phonemes.

Claim 17

The computer readable medium of claim 14, further including instructions causing the computer to determine the phonemic transformation weight based on the equation: ##EQU8## where n represents the number of states used to model a phoneme, v i is a heuristically set weight for each said state used to model the phoneme, and H(x i , y i ) represents relative entropy between probability mixtures of a state i of phoneme x and a state i of phoneme y.

Claim 18

The computer readable medium of claim 11, further including instructions for causing the computer to normalize the value indicating the likelihood of confusion between a first word and a second word.

Claim 19

The computer readable medium of claim 11, further including instructions causing the computer to calculate multiple Levinstein distances between the first and second phonemic transcriptions.

Claim 20

A computer system containing a process comprising: a memory that stores a confusability tool program and a transcripted phonemic lexicon, the confusability tool program including means for calculating a Levinstein distance between first and second phonemic transcriptions from the lexicon, wherein the Levinstein distance corresponds to edit operations required to transform the first transcription, obtaining a phonemic transformation weight for each edit operation of the Levinstein distance, and summing the obtained phonemic transformation weights to generate a value indicating the likelihood of confusion between the first and second phonemic transcriptions; a processor configured to execute the confusability tool program in the memory; and a display that provides an ordered list of entries, each entry including the first and second transcriptions and a cost based on the value indicating the likelihood of confusion between the first and second phonemic transcriptions.

Claim 21

A computer system containing a process comprising: a memory that stores a confusability tool program and a transcripted phonemic lexicon, the confusability tool program including means for calculating a Levinstein distance between first and second phonemic transcriptions from the lexicon, wherein the Levinstein distance corresponds to edit operations required to transform the first transcription, obtaining a phonemic transformation weight for each edit operation of the Levinstein distance, and summing the obtained phonemic transformation weights to generate a value indicating the likelihood of confusion between the first and second phonemic transcriptions; a processor configured to execute the confusability tool program in the memory; and a display that provides an ordered list of entries, each entry including the first and second transcriptions and a cost based on the value indicating the likelihood of confusion between the first and second phonemic transcriptions.

Claim 22

The computer system of claim 18, further including means for calculating cost by normalizing the minimum weighted Levinstein distance of multiple possible Levinstein distances between the first and second phonemic transcriptions.

Claim 23

The computer system of claim 18, further including means for obtaining the phonemic transformation weight based on phonological features of the first and second phonemic transcriptions.

Claim 24

The computer system of claim 18, further including means for obtaining the phonemic transformation weight based on relative entropy probability mixtures between states of Hidden Markov Models of phonemes of the first and second phonemic transcriptions.