US 2014/0006029 A1 · SYSTEMS AND METHODS FOR MODELING L1-SPECIFIC PHONOLOGICAL ERRORS IN COMPUTER-ASSISTED PRONUNCIATION TRAINING SYSTEM

Abstract

A non-transitory processor-readable medium storing code representing instructions to be executed by a processor includes code to cause the processor to receive acoustic data representing an utterance spoken by a language learner in a non-native language in response to prompting the language learner to recite a word in the non-native language and receive a pronunciation lexicon of the word in the non-native language. The pronunciation lexicon includes at least one alternative pronunciation of the word based on a pronunciation lexicon of a native language of the language learner. The code causes the processor to generate an acoustic model of the at least one alternative pronunciation in the non-native language and identify a mispronunciation of the word in the utterance based on a comparison of the acoustic data with the acoustic model. The code causes the processor to send feedback related to the mispronunciation of the word to the language learner.

Claims20

Dependent claims

A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:

receive acoustic data representing an utterance spoken by a language learner in a non-native language in response to prompting the language learner to recite a word in the non-native language;
receive a pronunciation lexicon of the word in the non-native language, the pronunciation lexicon of the word including at least one alternative pronunciation of the word determined based on a pronunciation lexicon of a native language of the language learner;
generate an acoustic model of the at least one alternative pronunciation of the word from the pronunciation lexicon of the word in the non-native language;
identify a mispronunciation of the word in the utterance based on a comparison of the acoustic data with the acoustic model;
and
send feedback related to the mispronunciation of the word to the language learner.

3 more elements

A method, comprising:

receiving acoustic data representing an utterance spoken by a language learner in a non-native language in response to prompting the language learner to recite a word in the non-native language;
generating an alternative pronunciation of the word based on a pronunciation lexicon of a native language of the language learner and phonetically annotated data associated with a native pronunciation of the word;
generating an acoustic model for the alternative pronunciation of the word;
identifying a mispronunciation of the word in the utterance in response to a speech recognition engine recognizing the acoustic data as part of the acoustic model;
and
sending feedback related to the mispronunciation of the word to the language learner in response to the identifying.

3 more elements

A method, comprising:

receiving a phrase having a plurality of words from a language learning module in response to the language learning module prompting a language learner to recite the phrase in a non-native language, the language learner having a native language;
generating a non-native lexicon including a set of alternative phrases having a probability greater than a threshold level of being spoken by the language learner when the language learner attempts to recite the phrase in the non-native language;
generating an acoustic model for each alternative phrase from the set of alternative phrases based on phonetically annotated data associated with a native recitation of each word from the plurality of words in the phrase;
identifying that the language learner recited an alternative phrase from the set of alternative phrases based on a comparison of the acoustic model for the alternative phrase and acoustic data representing an utterance spoken by the language learner in response to the language learning module prompting the language learner to recite the phrase in the non-native language;
identifying at least one word from the plurality of words in the phrase that was incorrectly recited by the language learner to produce the alternative phrase;
and
sending feedback to the language learner associated with the at least one word.

4 more elements

Dependencies are inferred from the claim text.

Systems and methods for modeling l1-specific phonological errors in computer-assisted pronunciation training system

Life of this patent

Systems and methods for modeling l1-specific phonological errors in computer-assisted pronunciation training system

Life of this patent

Abstract

Claims20