Speech processing apparatus and method

• US 20030163312A1
• Filed: 11/06/2002
• Published: 08/28/2003
• Est. Priority Date: 02/26/2002
• Status: Abandoned Application

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Sorrentino, Andrea
• Application Number:
  US10/288,285
• Publication Number:
  US 20030163312A1
• Time in Patent Office: Days
• US Class Current: 704/256
• CPC Class Codes: G10L 19/012 Comfort noise or silence coding
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10

Abstract

A speech recognition system is disclosed including a model generation unit (20) and a speech recognition unit (22). When signals are received from a microphone (7) the model generation unit (20) utilises the signals to generate hidden Markov models that are stored in a hidden Markov model database (24). Subsequently, when utterances are to be recognised, the speech recognition unit (22) utilises the stored hidden Markov models to associate an utterance with a word. When a new hidden Markov model is generated by the model generation unit (20) the new hidden Markov model is processed by a confusability checker (26) against the hidden Markov models already stored in the database (24). A value indicative of the likelihood of utterances corresponding to the new model being confused with previously stored models is determined by the confusability checker (26) directly from the parameters for the new hidden Markov model and the other hidden Markov models stored in the database (24). If this value indicates a high likelihood of words being confused, the new entry is deleted from the database (24) and a warning is output to the user.

Claims

1. An apparatus for measuring the likelihood that a series of signals corresponding to a first hidden Markov model will be identified with a second hidden Markov model using a data store configured to store a plurality of hidden Markov models, said models each comprising data identifying a series of probability density functions for a number of states, said apparatus comprising:

a calculator operable to calculate for each of the probability density functions of a first stored model stored in said data store values indicative of the logarithmic probability of a signal corresponding to a state identified by said stored probability density function would be identified with the states identified by probability density functions of a second stored model stored in said data store;

a determinator operable to determine from values calculated by said calculator, a set of transitions between said states of said first and second models stored in said data store where the states are associated with values indicative of the highest probabilities of signals corresponding to states of said first stored model being identified with states of said second stored model; and

an output unit operable to output as a measure of the likelihood of a series of signals corresponding to said first stored model being identified with said second stored model, the sum of calculated values for said set of transitions divided by the number of steps in said set of transitions.

2. Apparatus in accordance with claim 1, wherein said data store is configured to store models comprising data identifying a series of probability density functions for a set of states including a first state and a last state wherein said determinator is operable to determine a set of transitions from said first states of said models to said last states of said models associated with values indicative of the highest probabilities of signals corresponding to states of said first stored model being associated with said second stored model.

3. Apparatus in accordance with claim 2, wherein said data store is configured to store models comprising data identifying an allowable set of transitions within said series of states wherein said determinator is operable to determine the set of allowable transitions between states of said models associated with values indicative of the highest probabilities of signals corresponding to states of said first model being identified with said second model.

4. Apparatus in accordance with claim 3, wherein said data store is configured to store models comprising transition probability data associated with each of said allowable transitions, wherein said determinator is operable to determine a set of transitions associated with the highest probabilities of signals corresponding to states of said first model will occur and be matched to states in said second model utilizing a determined probability of said set of transitions occurring determined from said transition probabilities and wherein said measure of the likelihood of signals corresponding to said first model being identified with said second model comprises the sum of values for said set of transitions and logarithmic transition probabilities for said set of transitions divided by the number of steps in said set of transitions.

5. Apparatus in accordance with claim 1, wherein said data store is configured to store models each comprising data identifying a series of probability density functions corresponding to Gausian functions identified by a mean value and a variance wherein said calculator is operable to calculate said values for pairs of states from a said first and a said second models by determining

6. Apparatus in accordance with claim 5, wherein said determinator is operable to determine for pairs of probability density functions from a said first and second stored models a value CV_ij where

CV_ij=value_ij for i=1 j=1CV_ij=value_ij+min (CV_k1) for i≠

1 j≠

1

where value_ij is the logarithmic probability of a signal corresponding to state i of said first model is identified with state j of said second model calculated by said calculator and k→

i and l→

j are allowable transitions between states in said first and second model respectively.

7. Apparatus in accordance with claim 6, wherein said calculator is operable to calculate values corresponding to the sum of said calculated values for the logarithmic probability of a signal corresponding to state i of said first model is identified with state j of said second model and the logarithmic transition probability for the transitions from state k to state i of said first model and state l to state j of said second model.

8. Apparatus in accordance with claim 1, wherein said determinator is operable to determine said set of transitions by determining values utilizing dynamic programming.

9. A method of obtaining a measure of the likelihood that a series of signals corresponding to a first hidden Markov model will be identified with a second hidden Markov model, said models each comprising data identifying a series of probability density functions for a number of states, said method comprising:

calculating for each of said probability density functions of said first model values indicative of the logarithmic probability of a signal corresponding to a state identified by said probability density function would be identified with the states identified by said probability density functions of said second model;

determining from said calculated values a set of transitions between said states of said models where said states are associated with values indicative of the highest probabilities of signals corresponding to states of said first model being identified with states in said second model; and

outputting as a measure of the likelihood of a series of signals corresponding to said first hidden Markov model being identified with said second hidden Markov model, the sum of calculated values for said set of transitions divided by the number of steps in said set of transitions.

10. A method in accordance with claim 9, wherein each of said first and said second hidden Markov models comprise data identifying a series of probability density functions for a set of states including a first state and a last state wherein said determination of said set of transitions comprises determining a set of transitions from said first states of said models to said last states of said models.

11. A method in accordance with claim 10, wherein said first and said second hidden Markov models further comprise data identifying an allowable set of transitions within said series of states wherein said determination of a set of transitions comprises the determination of the set of allowable transitions between states of said models associated with values indicative of the highest probabilities of signals corresponding to states of said first model being identified with said second model.

12. A method in accordance with claim 11, wherein said first and said second hidden Markov models further comprise transition probability data associated with each of said allowable transitions, wherein said determination step comprises determining a set of transitions associated with the highest probabilities of signals corresponding to states of said first model will occur and be matched to states in said second model utilizing a determined probability of said set of transitions occurring determined from said transition probabilities and wherein said measure of the likelihood of signals corresponding to said first model being identified with said second model comprises the sum of values for said set of transitions and logarithmic transition probabilities for said set of transitions divided by the number of steps in said set of transitions.

13. A method in accordance with claim 9, wherein said first and second hidden Markov models each comprise data identifying a series of probability density functions corresponding to Gausian functions identified by a mean value and a variance wherein said calculation of said values for states of said first and said second models comprises a determination of

14. A method in accordance with claim 13, wherein said determination of the set of transitions between probability density functions comprises determining for pairs of probability density functions from said first and second models a value CV_ij where

Cv_ij=value_ij for i=1 j=1CV_ij=value_ij+min (CV_kl) for i≠

1 j≠

1

where value_ij is said calculated value for the logarithmic probability of a signal corresponding to state i of said first model is identified with state j of said second model and k→

i and l→

j are allowable transitions between states in said first and second model respectively.

15. A method in accordance with claim 14, wherein value_ij is the sum of said calculated value for the logarithmic probability of a signal corresponding to state i of said first model is identified with states of said second model and the logarithmic transition probability for the transitions k→

i and l→

j.

16. A method in accordance with any of claim 9, wherein said determination of said set of transitions comprises determining values utilizing dynamic programming.

17. A recording medium, storing computer implementable processor steps for causing a programmable computer to perform a method in accordance with claim 9.

18. A recording medium in accordance with claim 17 comprising a computer disc.

19. A recording medium in accordance with claim 17 comprising an electric signal transferred via the Internet.

20. A computer disc in accordance with claim 19, wherein said computer disc comprises an optical, magneto-optical or magnetic disc.