SPOKEN LANGUAGE LEARNING SYSTEMS

• US 20090258333A1
• Filed: 03/17/2009
• Published: 10/15/2009
• Est. Priority Date: 03/17/2008
• Status: Abandoned Application

• Current Assignee: Kai Yu
• Original Assignee: Kai Yu
• Inventors: YU, KAI
• Application Number:
  US12/405,434
• Publication Number:
  US 20090258333A1
• Time in Patent Office: Days
• US Class Current: 434/157
• CPC Class Codes: G09B 19/04 Speaking with audible presentation of the material to be studied G09B5/04
• CPC Class Codes: G09B 19/06 Foreign languages with audible presentation of material to be studied G09B5/04
• CPC Class Codes: G09B 5/04 with audible presentation of the material to be studied sound recording or reproducing G11B
• CPC Class Codes: G10L 15/18 using natural language modelling

Abstract

This invention relates to systems, methods and computer program code for facilitating learning of spoken languages. We describe a computing system to facilitate learning of a spoken language, the system comprising: a user interface to prompt a user of the system to produce a spoken language goal and to capture audio data comprising speech captured from said user in response; a speech analysis system to analyse said captured audio data to determine acoustic or linguistic pattern features of said captured audio data; a pattern matching system to match one or more subsets of said pattern features to a database of pattern features and to determine feedback data responsive to said match; and a feedback system to provide feedback to said user using said feedback data to facilitate said user to achieve said spoken language goal.

Claims

1. A computing system to facilitate learning of a spoken language, the system comprising:

a user interface to prompt a user of the system to produce a spoken language goal and to capture audio data comprising speech captured from said user in response;

a speech analysis system to analyse said captured audio data to determine acoustic or linguistic pattern features of said captured audio data;

a pattern matching system to match one or more subsets of said pattern features to a database of pattern features and to determine feedback data responsive to said match; and

a feedback system to provide feedback to said user using said feedback data to facilitate said user to achieve said spoken language goal.

2. A computing system as claimed in claim 1 wherein said database of pattern features is configured to store sets of linked data items, a said set of linked data items comprising a feature data item comprising a group of said pattern features for identifying an expected spoken response from said user to said spoken language goal, an instruction data item, said instruction data item comprising instruction data for instructing said user to improve or correct an error in said captured speech identified by said match, and a goal data item identifying said spoken language goal, such that said spoken language goal identifies a said set of said linked data items comprising a set of expected responses to said spoken language goal and a corresponding set of instruction data items for instructing said user to improve or correct an error in said captured speech, and wherein said pattern matching system is configured to match said pattern features of said captured audio data to pattern features of a said feature data item in a said set corresponding to said spoken language goal, and wherein said feedback comprises instructions to said user derived from said instruction data from a said instruction data item linked to said matched feature data item, whereby said instructions to said user correspond to an identified response from a set of expected responses to said spoken language goal.

3. A computing system as claimed in claim 1 wherein said speech analysis system comprises an acoustic pattern analysis system to identify one or more of phones, words and sentences from said spoken language in said captured audio data and to provide associated confidence data, and wherein said acoustic pattern features comprise one or more of phones, words and sentences and associated confidence scores, and

wherein said acoustic pattern analysis system is further configured to identify prosodic features in said captured audio data, a said prosodic feature comprising a combination of a determined fundamental frequency of a segment of said captured audio corresponding to a said phone or word, a duration of said segment of captured audio, and an energy in said segment of captured audio, and wherein said acoustic pattern features include said prosodic features.

4. A computing system as claimed in claim 3 wherein said speech analysis system includes a linguistic pattern analysis system to match a grammar employed by said user to one or more of a plurality of types of grammatical structure, and wherein said linguistic pattern features comprise grammatical pattern features of said captured speech, and

wherein one or both of said plurality of types of grammatical structure and said identified phones, words or sentences include erroneous types of grammatical structure or phones, words or sentences.

5. A computing system as claimed in claim 3 wherein said linguistic pattern analysis system is configured to identify key words of a set of key words, and wherein said acoustic pattern analysis system is configured to provide confidence data for said identified key words, wherein said pattern features include confidence scores for said identified key words.

6. A computing system as claimed in claim 1 wherein said speech analysis system comprises a speech recognition system including both an acoustic model to provide said acoustic pattern features and a linguistic model to provide said linguistic pattern features.

7. A computing system as claimed in claim 6 wherein said speech recognition system is configured to provide data identifying one or both of phone and word boundaries, and wherein said pattern features include features of said portions of said captured audio data segmented at said phone or word boundaries.

8. A computing system as claimed in claim 1 wherein said feedback data comprises an index to index a selected instruction record of a set of instruction records responsive to a combination of a said match and said goal, said instruction recording comprising instruction data for instructing said user to improve or correct an error in said captured speech identified by said match, and wherein said feedback comprises instructions to said user derived from said instruction data to improve or correct said user'"'"'s speech.

9. A computing system as claimed in claim 1 wherein said feedback data is hierarchically arranged having a hierarchy including at least an acoustic level and a linguistic level, and wherein said feedback system is configured to select a level in said hierarchy responsive to one or both of said spoken language goal and a level of determined skilled in said spoken language of said user, and

wherein said feedback to said user includes a score, wherein said score is determined by modifying a value derived from a goodness of said match by a mapping function, and wherein said mapping function is determined such that scores from said computer system correlate with corresponding scores by humans.

10. A computing system as claimed in claim 1 wherein said spoken language comprises a tonal language, and wherein said feedback data comprises pitch trajectory data, and

wherein said feedback to said user comprises a graphical representation of said user'"'"'s pitch trajectory for a phone, word or sentence of said tonal language and a graphical indication of a corresponding desired pitch trajectory.

11. A computing system as claimed in claim 1 further comprising a historical data store to store historical data from a plurality of different users comprising one or both of said determined acoustic pattern features and said determined linguistic pattern features, and a system to identify within said historical data new pattern features not within said database of pattern features and to add said new pattern features to said database of pattern features responsive to said identification, and

further comprising a system to add new feedback data to said database corresponding to said new pattern features, and wherein said new feedback data comprises data captured from one or more users by questioning a said user as to how an error in said captured speech associated with a new pattern feature was overcome.

12. A computer system as claimed in claim 1 to facilitate testing of a said spoken language in addition to or instead of facilitating learning of said spoken language, wherein said feedback system is configured to produce a test result in addition to or instead of providing feedback to said user.

13. A carrier carrying computer program code to, when running, facilitate learning of a spoken language, the code comprising code to implement:

a user interface to prompt a user of the system to produce a spoken language goal and to capture audio data comprising speech captured from said user in response;

a speech analysis system to analyse said captured audio data to determine acoustic or linguistic pattern features of said captured audio data;

a pattern matching system to match one or more subsets of said pattern features to a database of pattern features and to determine feedback data responsive to said match; and

a feedback system to provide feedback to said user using said feedback data to facilitate said user to achieve said spoken language goal.

14. A speech processing system for processing speech and outputting instruction data items responsive to identified acoustic and linguistic patterns in said speech, the system comprising:

a front end processing module, having an input to receive analogue speech data, an analogue to digital converter to convert said analogue speech data into digital speech data, means for performing a Fourier analysis on said digital speech data to provide a frequency spectrum of said digital speech data, means for generating feature vector data and prosodic feature data from said frequency spectrum of said digital speech data, said prosodic feature data comprising a combination of a determined fundamental frequency of a segment of said digital speech data corresponding to a phone or a word, a duration of said digital speech data and an energy in said segment of digital speech data;

a statistical speech recognition module coupled to said front end processing module, having an input to receive said feature vector data and said prosodic feature data, and comprising a lexicon, an acoustic model, and a language model,

said lexicon having an input to receive said prosodic feature data, a memory storing a pre-determined mapping of said prosodic feature data to acoustic data items, and an output to output said acoustic data items, said acoustic data items being one or more of data defining phones and data defining words and data defining syllables,

said acoustic model having an input to receive said acoustic data items, said feature vector data and said prosodic feature data, and comprising a probabilistic model operable to determine the probability of said acoustic data items existing in said feature vector data and said prosodic feature data, selecting said acoustic data items with a highest match probability and outputting said acoustic data items with said highest match probability,

said language model having an input to receive said acoustic data items from said lexicon and an output to output a language data item, said language data item comprising data identifying one or more of phones, words and syllables in said digital speech data, the language model comprising means to analyse at least one previously generated language data item and said acoustic data items from said lexicon and to generate a further said language data item for output;

an acoustic pattern feature extraction module coupled to said statistical speech recognition module and said front end processing module, having an input to receive said acoustic data items from said statistical speech recognition module, having an input to receive said prosodic feature data from said front end processing module, and means for determining acoustic features of said acoustic data items from said prosodic data items, said acoustic features comprising pitch trajectory, and outputting acoustic feature data items defining said acoustic features;

a linguistic pattern feature extraction module coupled to said statistical speech recognition module and having an input to receive said language data items, a memory storing predefined linguistic structures, said linguistic structures storing at least one of grammatical patterns and semantic patterns, means for matching said language data items to said predefined linguistic structures, and means for outputting a linguistic structure data item comprising data characterising a linguistic structure of said language data items according to said predefined linguistic structures in said linguistic structure memory;

a pattern feature memory, configured to store a plurality of pattern-instruction pairs, a pattern item in said pattern-instruction pair defining a language learning goal and an instruction in said pattern-instruction pair defining an instruction item responsive to said pattern item defining a language learning goal;

a pattern matching module coupled to said acoustic pattern feature extraction module and said linguistic pattern feature extraction module and said pattern feature memory, having an input to receive said acoustic feature data items from said acoustic pattern feature extraction module, having an input to receive said linguistic structure data items from said linguistic pattern feature extraction module, and

means for matching said acoustic feature data items to said plurality of pattern-instruction pairs in said pattern feature memory by comparing said pattern items in said pattern-instruction pair and said acoustic feature data items output from said acoustic pattern feature extraction module,

means for matching said linguistic structure data items to said plurality of pattern-instruction pairs by comparing said linguistic structure data items with said pattern items in said plurality of acoustic and linguistic pattern-instruction pairs,

outputting said instruction items responsive to said pattern items in said plurality of acoustic and linguistic pattern-instruction pairs.

15. A speech processing system as claimed in claim 14 wherein said pattern item in said pattern-instruction pair defines an erroneous language learning goal and said instruction in said pattern-instruction pair defines an instruction item responsive to said pattern item, said instruction item comprising data for instructing correction of at least one of said acoustic feature data items and said linguistic structure data items matching said pattern item defining an erroneous language learning goal.

16. A speech processing system as claimed in claim 14 wherein said feature vector data generated in said front end processing module is perceptual linear prediction (PLP) feature data.

17. A speech processing system as claimed in claim 14 wherein said acoustic features determined in said acoustic pattern feature extraction module further comprise at least one of duration and energy and confidence score of said acoustic data items.

18. A speech processing system as claimed in claim 14 wherein said probabilistic model in said acoustic model is a Hidden Markov Model.

19. A speech processing system as claimed in claim 14 further comprising an adaptation module, said adaptation module comprising an historical memory configured to store historical data items from a plurality of different users comprising one or both of said acoustic feature data items and said linguistic structure data items, and means to identify within said historical data items new pattern items not within said pattern feature memory and to add said new pattern items to said pattern-instruction pairs in said pattern feature memory responsive to said identification.

20. A speech processing system as claimed in claim 19 wherein the adaptation module is further operable to add new instruction items to said pattern-instruction pairs in said pattern feature memory, wherein said new instruction items comprise data captured from new digital speech data generated from said users defining a response associated with said new pattern items added to said pattern feature memory.