Selective noise/channel/coding models and recognizers for automatic speech recognition

Abstract

An apparatus and method for the robust recognition of speech during a call in a noisy environment is presented. Specific background noise models are created to model various background noises which may interfere in the error free recognition of speech. These background noise models are then used to determine which noise characteristics a particular call has. Once a determination has been made of the background noise in any given call, speech recognition is carried out using the appropriate background noise model.

What is claimed is: 
     
       1. A method for the robust recognition of speech in a noisy environment, comprising the steps of:
 receiving the speech; 
 recording an amount of data related to the a noisy environment, to yield recorded data; 
 analyzing the recorded data; 
 selecting at least one appropriate a background noise model on the basis of based on the recorded data, to yield a selected background noise model; and 
 performing speech recognition with the at least one selected background noise model. 
 
     
     
       2. The method according to of  claim 1 , further comprising the step of:
 modeling at least one a background noise in a the noisy environment to create at least one the background noise model. 
 
     
     
       3. The method according to of  claim 1 , further comprising the step of:
 determining the a correctness of the at least one selected background noise model, wherein if when the at least one selected background noise model is determined to be incorrect, the method comprises loading at least one other another background noise model for use in the step of performing speech recognition. 
 
     
     
       4. The method according to of  claim 1 , further comprising the step of:
 constructing a background noise database for use in analyzing the recorded data on the noisy environment. 
 
     
     
       5. The method according to of  claim 4 , wherein the background noise database is dynamically updated for each location from which data is recorded. 
     
     
       6. The method according to of  claim 1 , wherein the step of analyzing the recorded data is accomplished by using at least one of a plurality of signal information. 
     
     
       7. The method according to of  claim 1 , wherein the step of analyzing the recorded data is accomplished by using a correct match percentage for a plurality of background noise models determined by an input response. 
     
     
       8. The method according to of  claim 1 , wherein the step of performing speech recognition is accomplished by at least one a recognizer. 
     
     
       9. A method for improving recognition of speech subjected to noise, the method comprising the steps of:
 sampling a connection noise to yield sampled connection noise; 
 searching a database for a noise model most closely matching that matches the sampled connection noise to yield a matching noise model; and 
 applying the most closely matching noise model to a speech recognition process. 
 
     
     
       10. The method according to of  claim 9 , wherein the connection noise includes at least comprises one of city noise, motor vehicle noise, truck noise, traffic noise, airport noise, subway train noise, cellular interference noise, channel condition noise, telephone microphone characteristics noise, cellular coding noise, and Internet network connection noise. 
     
     
       11. The method according to of  claim 9 , wherein the noise model is constructed by modeling at least one the connection noise. 
     
     
       12. The method according to of  claim 9 , wherein when a speech recognition error rate is determined to be above a predetermined level, the system substitutes the applied method further comprises substituting the matching noise model by applying at least one other a second noise model. 
     
     
       13. The method according to of  claim 9 , wherein at least one a speech recognition unit is used when applying the matching noise model. 
     
     
       14. A speech recognition apparatus comprising:
 a speech recognizer;   a database having stored thereon templates of a plurality of background noises; and   an identifier that identifies, via a processor, a background noise template from the plurality of background noise templates, the background noise template matching a background noise from an input signal, to yield a matching background noise template, wherein the speech recognizer recognizes speech from the input signal with reference to the matching background noise template.   
     
     
       15. The speech recognition apparatus of claim 14, wherein the identifier compares hidden Markov models of the plurality of background noise templates to a hidden Markov model of the background noise from the input signal. 
     
     
       16. The speech recognition apparatus of claim 14, wherein the identifier identifies a portion of the input signal that is unlikely to contain speech, to yield an identified portion, wherein the identified portion is used as the background noise. 
     
     
       17. The speech recognition apparatus of claim 14, wherein the identifier, when a plurality of background noise templates match the background noise, selects a template selected in a prior iteration as the matching background noise template. 
     
     
       18. The speech recognition apparatus of claim 14, further comprising:
 a restrictor that restricts a number of candidate templates based on geographic information associated with the input signal;   a comparer that compares the background noise to the restricted candidate templates to yield a comparison; and   a selector that selects the matching background noise template based on the comparison.   
     
     
       19. The speech recognition apparatus of claim 14, further comprising:
 a restrictor that restricts a number of candidate templates based on time of day information associated with the input signal to yield restricted candidate templates;   a comparer that compares the background noise to the restricted candidate templates to yield a comparison; and   a selector that selects the matching background noise template based on the comparison.   
     
     
       20. The speech recognition apparatus of claim 14, further comprising:
 a restrictor that restricts a number of candidate templates based on an identifier of a user at a location from which the input signal is captured to yield restricted candidate templates;   a comparer that compares the background noise to the restricted candidate templates to yield a comparison; and   a selector that selects the matching background noise template based on the comparison.   
     
     
       21. The speech recognition apparatus of claim 14, further comprising a microphone to capture the input signal. 
     
     
       22. The speech recognition apparatus of claim 14, further comprising a telephone to capture the input signal. 
     
     
       23. A speech recognition apparatus comprising:
 a database having stored thereon templates of a plurality of background noises; and   a controller that identifies a background noise template, from the templates of the plurality of background noise templates, that matches background noise from a received input signal, to yield a matching background noise template, and supplies the matching background noise template to a speech recognizer.   
     
     
       24. The speech recognition apparatus of claim 23, further comprising the speech recognizer. 
     
     
       25. The speech recognition apparatus of claim 23, further comprising a microphone to capture the input signal. 
     
     
       26. The speech recognition apparatus of claim 23, further comprising a telephone to capture the input signal. 
     
     
       27. A method comprising:
 sampling a noise signal to yield a sampled noise signal;   searching a database for a noise model matching the sampled noise signal to yield a matching noise model; and   applying the matching noise model to a speech recognition process.   
     
     
       28. The method of claim 27, wherein the searching comprises comparing hidden Markov models in the database to a hidden Markov model of the sampled noise signal. 
     
     
       29. The method of claim 27, further comprising, prior to the sampling, isolating the noise signal from an input signal. 
     
     
       30. The method of claim 27, further comprising, when a plurality of stored noise models match the sampled noise signal, selecting one of the plurality of stored noise models as the matching noise model according to a selection made in a prior iteration. 
     
     
       31. The method of claim 27, wherein the searching comprises:
 restricting a set of candidate noise models based on geographic information associated with the sampled noise signal, to yield a restricted set of candidate noise models;   comparing the sampled noise signal to the restricted set of candidate noise models, to yield a comparison; and   selecting the matching noise model based on the comparison.   
     
     
       32. The method of claim 27, wherein the searching comprises:
 restricting a set of candidate noise models based on time of day information associated with the sampled noise signal, to yield a restricted set of candidate noise models;   comparing the sampled noise signal to the restricted set of candidate noise models, to yield a comparison; and   selecting the matching noise model based on the comparison.   
     
     
       33. The method of claim 27, wherein the searching comprises:
 restricting a set of candidate noise models based on an identifier of a user at a location from which the sampled noise signal is captured, to yield a restricted set of candidate noise models;   comparing the sampled noise signal to the restricted set of candidate noise models, to yield a comparison; and   selecting the matching noise model based on the comparison.   
     
     
       34. A speech recognition method, comprising:
 identifying a background noise component from an input signal;   comparing the background noise component to a plurality of previously-stored noise models, to yield a comparison;   selecting a noise model from the plurality of previously-stored noise models based on the comparison, to yield a selected noise model; and   performing speech recognition on the input signal with reference to the selected noise model.   
     
     
       35. The speech recognition method of claim 34, further comprising:
 identifying a subsequent background noise component from the input signal;   comparing the subsequent background noise component to the plurality of previously-stored noise models, to yield a second comparison;   selecting a second noise model from the plurality of previously-stored noise models based on the second comparison, to yield a second selected noise model; and   performing speech recognition on the input signal with reference to second selected noise model.   
     
     
       36. The speech recognition method of claim 34, further comprising:
 when speech recognition fails, selecting a second noise model from the plurality of previously-stored noise models based on the second comparison, to yield a second selected noise model; and   performing speech recognition on the input signal with reference to the second selected noise model.   
     
     
       37. The speech recognition method of claim 34, further comprising, wherein the identifying occurs while prompting a user with an introductory message. 
     
     
       38. The speech recognition method of claim 34, wherein the comparing uses hidden Markov models of the plurality of previously-stored noise models and a hidden Markov model of the background noise component. 
     
     
       39. The speech recognition method of claim 34, further comprising, when a plurality of noise models from the plurality of previously-stored noise models match the background noise component, selecting one of the plurality of previously-stored noise models as a most closely matching noise model according to a selection made in a prior iteration. 
     
     
       40. The speech recognition method of claim 34, wherein the comparing and selecting comprise:
 restricting a set of candidate noise models based on geographic information associated with the background noise component, to yield a restricted set of candidate noise models;   comparing the background noise component to the restricted set of candidate noise models, to yield a second comparison; and   selecting the matching noise model based on the second comparison.   
     
     
       41. The speech recognition method of claim 34, wherein the comparing and selecting comprise:
 restricting a set of candidate noise models based on time of day information associated with the background noise component, to yield a restricted set of candidate noise models;   comparing the background noise component to the restricted set of candidate noise models, to yield a second comparison; and   selecting the matching noise model based on the second comparison.   
     
     
       42. The speech recognition method of claim 34, wherein the comparing and selection comprise:
 restricting a set of candidate noise models based on an identifier of a user at a location from which the input signal is captured, to yield a restricted set of candidate noise models;   comparing the background noise component to the restricted set of candidate noise models, to yield a second comparison; and   selecting a closely matching noise model based on the second comparison.