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Example 8: Auralog Tell-Me-More Speech Recognition Test

2008/08/29 1 comment

How usable is the Auralog Speech Recognition for language learning? This test, by a non-native speaker of English, gives some authentic data points.

image

The test shows: Auralog Speech Recognition

  1. can be easily tripped up; however, by errors that  a non-native language learner would not normally make
  2. more concerning is that the built-in AI, instead of e.g. escalating to additional feedback or help, like the pronunciation waveforms (which in itself seem to encourage only repeated attempts to mimic a given intonation, while not being fine-grained enough to spot mispronunciations on a word, let alone letter level) – lowers the requirements when a speaker repeatedly fails (which in extreme seems to amount to “waving through” any utterance).
  3. the preset dialogue – only few exercises including wrong answer options, most exercises testing only a comprehensible pronunciation of a given reading text which makes the exercise much easier for the built-in speech recognition, but also much less realistic and useful for a language learner (or more of a reading exercise).
Categories: e-languages, English, Institution-is-Aston-University, service-is-evaluating-learning-tools, Speaking Tags: ,

Protected: Course series on 20th century cultural history based on “100 deutsche Jahre” documentaries

2006/06/13 Enter your password to view comments.

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Categories: e-languages, German, learning-usage-samples, Slideshows Tags: , , , , ,

Using home-brew NLP regular expressions to automate question generation for learning material creation

2006/05/24 Leave a comment
    1. The trpQuizGenerator, from which the screenshots below are taken,
      1. is an attempt to facilitate, speed up, automate question generation for foreign language learning by collecting a regular expressions, reflecting typical patterns that cause difficulties for language learners in a number of L2 – inspired by common 1st and 2nd year textbooks:  trpQuizGenerator-NLP-samples-German-Italian-Spanish
      2. German: differentiation between Dative and Accusative case personal pronouns
      3. Italian: contraction of article and preposition
      4. Spanish: demonstrative pronouns.
      5. Some more rather arbitrary, but easily implemementable examples for ESL:
        1. Numbers: ―Much/many‖ dichotomy
        2. which/who‖ relative pronoun dichotomy: Difficult for German students of English which has no such
          dichotomy for innate beings/things, but whose (antiquated) relative pronoun ―Welch‖ as a false friend
          of which tends to lead to a wrong preference of ―which‖ to ―who‖ by German speakers.
        3. Sub clauses/tenses: if clauses up to period/comma, giving the number of words as hints. Would
          require a delegate.
      6. Regular expressions in .Net have a number of advanced features that makes the platform a good choice for this enterprise: trpQuizGenerator-NLP-sample-German-personalpronomen-akk-dat
      7. The resulting texts can be e.g. easily delivered as formative assessment exercises  to students using trpQuiz.dot.
    2. Update:
      1. In a  much more recent approach to the same automation problem, I am trying to repurpose well-established existing NLP-platforms for question generation.
      2. However, compared with the above customized approach, to transform the built-in, not SLA-specific NLP recognition I have found so far taking not only much more work for reformatting for delivery, but also more creativity, or willingness to put up with limitations when it comes to homing in on typical learner problems.
Categories: English, German, Italian, service-is-learning-materials-creation, service-is-programming, Spanish Tags: , , , ,

Collaborative timeline activity for face-to-face classes on history

2006/03/15 Leave a comment
  1. An easily produced and repeated classroom activity, originally developed for listening comprehension and speaking practice in  language classes, based on filling out collaboratively a timeline spreadsheet in the digital audio lab:
    1. Listen and process/write:
      1. Advanced German class listens to segments of an authentic German cultural history documentary from the authentic German TV series “100 deutsche Jahre” (which follows a single topic throughout 20th century German history).
      2. And each student enters notable summaries of events with their time of occurrence into a spreadsheet
      3. that the teacher
      4. has at beginning of activity distributed to each individual student using the digital audio labs file management features
      5. and after listening collects from students, merges, either with student author data or an anonymous student identifier (for corrections), into an excel timeline spreadsheet
      6. and visualizes the collaborative outcome as an easily collated timeline on the projector to the entire class.
    2. Speaking: Discuss!
      1. Identify what are the gravity points for the comprehension of the video by the class: Why are these events deemed important?
      2. What are the outliers? Criticism? Justification?
      3. Also correct language errors in the  student output.
    3. nexus_timeline_excel_100_deutsche_jahre
      1. In early 2006, there was no Excel web app – collaboration likely has become simpler now
        1. launch link to publically editable spreadsheet to class
        2. visualize using excel web app charts
Categories: Charts, classroom-activities, digital-humanities, German, learning-materials, Listening, Speaking Tags: , , ,