Assessment of emerging reading skills in young native speakers and language learners

  • Authors:

  • Patti Price;Joseph Tepperman;Markus Iseli;Thao Duong;Matthew Black;Shizhen Wang;Christy Kim Boscardin;Margaret Heritage;P. David Pearson;Shrikanth Narayanan;Abeer Alwan

  • Affiliations:

  • PPrice Speech and Language Technology, 420 Shirley Way, Menlo Park, CA 94025, USA;Dept. of Electrical Engineering, University of Southern California, EEB 400, 3740 McClintock Ave., Los Angeles, CA 90089, USA;Dept. of Electrical Engineering, Henry Samueli School of Engineering and Applied Science 63-134 Engr. IV, UCLA, 405 Hilgard Ave., Box 951594, Los Angeles, CA 90095-1594, USA;Graduate School of Education, 1501 Tolman Hall #5519, University of California, Berkeley, Berkeley, CA 94720-1670, USA;Dept. of Electrical Engineering, University of Southern California, EEB 400, 3740 McClintock Ave., Los Angeles, CA 90089, USA;Electrical Engineering, University of California, Los Angeles, UCLA, CA 90095, USA;School of Medicine, Office of Medical Education, University of California, San Francisco, 521 Parnassus Avenue, Room C-254, San Francisco, CA 94143-0410, USA;CRESST/UCLA, 10945 Le Conte Ave, Los Angeles, CA 90095-7150, USA;Graduate School of Education, 1501 Tolman Hall #1670, University of California, Berkeley, Berkeley, CA 94720-1670, USA;Dept. of Electrical Engineering, University of Southern California, EEB 400, 3740 McClintock Ave., Los Angeles, CA 90089, USA;Dept. of Electrical Engineering, Henry Samueli School of Engineering and Applied Science 66-147G Engr. IV, UCLA, 405 Hilgard Ave., Box 951594, Los Angeles, CA 90095-1594, USA

  • Venue:
  • Speech Communication
  • Year:
  • 2009

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Abstract

To automate assessments of beginning readers, especially those still learning English, we have investigated the types of knowledge sources that teachers use and have tried to incorporate them into an automated system. We describe a set of speech recognition and verification experiments and compare teacher scores with automatic scores in order to decide when a novel pronunciation is best viewed as a reading error or as dialect variation. Since no one classroom teacher is expected to be familiar with as many dialect systems as might occur in an urban classroom, making progress in automated assessments in this area can improve the consistency and fairness of reading assessment. We found that automatic methods performed best when the acoustic models were trained on both native and non-native speech, and argue that this training condition is necessary for automatic reading assessment since a child's reading ability is not directly observable in one utterance. We also found assessment of emerging reading skills in young children to be an area ripe for more research!