System and method of speech recognition for non-native speakers of a language
View Patent ↗An accent compensative speech recognition system and related methods for use with a signal processor generating one or more feature vectors based upon a voice-induced electrical signal are provided. The system includes a first-language acoustic module that determines a first-language phoneme sequence based upon one or more feature vectors, and a second-language lexicon module that determines a second-language speech segment based upon the first-language phoneme sequence. A method aspect includes the steps of generating a first-language phoneme sequence from at least one feature vector based upon a first-language phoneme model, and determining a second-language speech segment from the first-language phoneme sequence based upon a second-language lexicon model.
1. A method for speech recognition of input speech in a language from a non-native speaker, the method comprising acts of:
generating one or more feature vectors based upon one or more voice-induced electrical signals that result from the input speech;
generating a first-language phoneme sequence from the one or more feature vectors based upon a first-language acoustic model, wherein the first-language acoustic model corresponds to a first language;
determining a second-language speech segment from the first-language phoneme sequence based upon a second-language lexicon model, wherein the second-language lexicon model corresponds to a second language that is different from the first language;
determining a confidence score associated with a combination of the first-language acoustic model and the second-language lexicon model; and
selecting the first-language acoustic model from a plurality of acoustic models based at least in part on the determined confidence score, each of the plurality of acoustic models corresponding to a different respective language.
2. The method of claim 1 , wherein the act of selecting the first-language acoustic model from the plurality of acoustic models comprises selecting the first-language acoustic model based at least in part of the determined confidence score being greater than another confidence score associated with a combination of another first-language acoustic model and the second-language lexicon model.
3. The method of claim 1 , wherein the confidence score is associated with the second-language speech segment.
4. The method of claim 3 , wherein the confidence score is based on a probability assessment that the first-language phoneme sequence generated based upon the first-language acoustic model corresponds to the second-language speech segment determined using the second-language lexicon model.
5. The method of claim 1 , further comprising an act of:
generating a speech recognition output for subsequent input speech from the non-native speaker using the first-language acoustic model.
6. The method of claim 1 , wherein the first-language acoustic model is associated with a native language of the non-native speaker and the second-language lexicon model is associated with a spoken language of the input speech.
7. At least one computer-readable medium encoded with instructions that, when executed by at least one computer system, perform a method for speech recognition of input speech in a language from a non-native speaker, the method comprising acts of:
generating, based upon a first-language acoustic model, a first-language phoneme sequence from one or more feature vectors, the one or more feature vectors being based upon one or more voice-induced electrical signals that result from the input speech, wherein the first-language acoustic model corresponds to a first language;
determining a second-language speech segment from the first-language phoneme sequence based upon a second-language lexicon model, wherein the second-language lexicon model corresponds to a second language that is different from the first language;
determining a confidence score associated with a combination of the first-language acoustic model and the second-language lexicon model; and
selecting the first-language acoustic model from a plurality of acoustic models based at least in part on the determined confidence score, each of the plurality of acoustic models corresponding to a different respective language.
8. The at least one computer-readable medium of claim 7 , wherein the act of selecting the first-language acoustic model from the plurality of acoustic models comprises selecting the first-language acoustic model based at least in part of the determined confidence score being greater than another confidence score associated with a combination of another first-language acoustic model and the second-language lexicon model.
9. The at least one computer-readable medium of claim 7 , wherein the confidence score is associated with the second-language speech segment.
10. The at least one computer-readable medium of claim 8 , wherein the confidence score is based on a probability assessment that the first-language phoneme sequence generated based upon the first-language acoustic model corresponds to the second-language speech segment determined using the second-language lexicon model.
11. The at least one computer-readable medium of claim 7 , wherein the method further comprises an act of:
generating a speech recognition output for subsequent input speech from the non-native speaker using the first-language acoustic model.
12. The at least one computer-readable medium of claim 7 , wherein the first-language acoustic model is associated with a native language of the non-native speaker and the second-language lexicon model is associated with a spoken language of the input speech.
13. An apparatus for speech recognition of input speech in a language from a non-native speaker, the apparatus comprising:
at least one computer-readable medium encoded with instructions; and
at least one processing unit coupled to the at least one computer-readable medium, wherein upon execution of the instructions by the at least one processing unit, the at least one processing unit:
generates one or more feature vectors based upon one or more voice-induced electrical signals that result from the input speech;
generates a first-language phoneme sequence from the one or more feature vectors based upon a first-language acoustic model, wherein the first-language acoustic model corresponds to a first language;
determines a second-language speech segment from the first-language phoneme sequence based upon a second-language lexicon model, wherein the second-language lexicon model corresponds to a second language that is different from the first language;
determines a confidence score associated with a combination of the first-language acoustic model and the second-language lexicon model; and
selects the first-language acoustic model from a plurality of acoustic models based at least in part on the determined confidence score, each of the plurality of acoustic models corresponding to a different respective language.
14. The apparatus of claim 13 , wherein the at least one processing unit:
selects the first-language acoustic model based at least in part of the determined confidence score being greater than another confidence score associated with a combination of another first-language acoustic model and the second-language lexicon model.
15. The apparatus of claim 13 , wherein the confidence score is associated with the second-language speech segment.
16. The apparatus of claim 15 , wherein the confidence score is based on a probability assessment that the first-language phoneme sequence generated based upon the first-language acoustic model corresponds to the second-language speech segment determined using the second-language lexicon model.
17. The apparatus of claim 13 , wherein the at least one processing unit:
generates a speech recognition output for subsequent input speech from the non-native speaker using the first-language acoustic model.
18. The apparatus of claim 13 , wherein the first-language acoustic model is associated with a native language of the non-native speaker and the second-language lexicon model is associated with a spoken language of the input speech.