Two methods for assessing oral reading prosody

We compare two types of models to assess the prosody of children's oral reading. Template models measure how well the child's prosodic contour in reading a given sentence correlates in pitch, intensity, pauses, or word reading times with an adult narration of the same sentence. We evaluate template models directly against a common rubric used to assess fluency by hand, and indirectly by their ability to predict fluency and comprehension test scores and gains of 10 children who used Project LISTEN's Reading Tutor; the template models outpredict the human assessment. We also use the same set of adult narrations to train generalized models for mapping text to prosody, and use them to evaluate children's prosody. Using only durational features for both types of models, the generalized models perform better at predicting fluency and comprehension posttest scores of 55 children ages 7--10, with adjusted R2 of 0.6. Such models could help teachers identify which students are making adequate progress. The generalized models have the additional advantage of not requiring an adult narration of every sentence.

[1]  Rebekah George Benjamin,et al.  Text Complexity and Oral Reading Prosody in Young Readers , 2010 .

[2]  Jack Mostow,et al.  Using Automated Questions to Assess Reading Comprehension, Vocabulary, and Effects of Tutorial Interventions , 2004 .

[3]  Paula J Schwanenflugel,et al.  Prosody of Syntactically Complex Sentences in the Oral Reading of Young Children. , 2006, Journal of educational psychology.

[4]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[5]  Shrikanth S. Narayanan,et al.  A text-free approach to assessing nonnative intonation , 2007, INTERSPEECH.

[6]  Steven A. Stahl,et al.  Fluency: A review of developmental and remedial practices. , 2003 .

[7]  Alan W. Black,et al.  Unit selection in a concatenative speech synthesis system using a large speech database , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[8]  Mitch Weintraub,et al.  Automatic evaluation and training in English pronunciation , 1990, ICSLP.

[9]  David M. Magerman Statistical Decision-Tree Models for Parsing , 1995, ACL.

[10]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[11]  Jack Mostow,et al.  Automatically Assessing Oral Reading Fluency in a Computer Tutor that Listens , 2004 .

[12]  P. Boersma Praat : doing phonetics by computer (version 5.1.05) , 2009 .

[13]  Abeer Alwan,et al.  A Generative Student Model for Scoring Word Reading Skills , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Jian Cheng,et al.  Automatic evaluation of reading accuracy: assessing machine scores , 2007, SLaTE.

[15]  Shrikanth S. Narayanan,et al.  Investigating automatic assessment of reading comprehension in young children , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  G. Tindal,et al.  Oral Reading Fluency Norms: A Valuable Assessment Tool for Reading Teachers , 2006 .

[17]  Paula J Schwanenflugel,et al.  A Longitudinal Study of the Development of Reading Prosody as a Dimension of Oral Reading Fluency in Early Elementary School Children. , 2008, Reading research quarterly.

[18]  S. Deno,et al.  Curriculum-Based Measurement: The Emerging Alternative , 1985, Exceptional children.

[19]  Paul Boersma,et al.  Praat, a system for doing phonetics by computer , 2002 .

[20]  Joseph E. Beck,et al.  Using Knowledge Tracing in a Noisy Environment to Measure Student Reading Proficiencies , 2006, Int. J. Artif. Intell. Educ..

[21]  H. Lee Swanson,et al.  Repeated Reading versus Continuous Reading: Influences on Reading Fluency and Comprehension , 2007 .

[22]  Ronald A. Cole,et al.  Highly accurate children's speech recognition for interactive reading tutors using subword units , 2007, Speech Commun..

[23]  Karin Vermeulen,et al.  Paths to Reading Comprehension in At-Risk Second-Grade Readers , 2006, Journal of learning disabilities.

[24]  Jan P. H. van Santen,et al.  Assignment of segmental duration in text-to-speech synthesis , 1994, Comput. Speech Lang..

[25]  Jack Mostow,et al.  Can a Computer Listen for Fluctuations in Reading Comprehension? , 2007, AIED.

[26]  Jack Mostow,et al.  How Who Should Practice: Using Learning Decomposition to Evaluate the Efficacy of Different Types of Practice for Different Types of Students , 2008, Intelligent Tutoring Systems.

[27]  Paula J Schwanenflugel,et al.  Becoming a fluent and automatic reader in the early elementary school years. , 2006, Reading research quarterly.

[28]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[29]  D. Langenberg Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction , 2000 .

[30]  Steven A. Stahl,et al.  Teaching Children to Become Fluent and Automatic Readers , 2006, Journal of literacy research : JLR.

[31]  Jack Mostow,et al.  Detecting prosody improvement in oral rereading , 2009, SLaTE.

[32]  Shrikanth S. Narayanan,et al.  Automatic detection and classification of disfluent reading miscues in young children's speech for the purpose of assessment , 2007, INTERSPEECH.

[33]  Jack Mostow,et al.  A Prototype Reading Coach that Listens , 1994, AAAI.

[34]  Albert T. Corbett,et al.  Evaluation of an Automated Reading Tutor That Listens: Comparison to Human Tutoring and Classroom Instruction , 2003 .

[35]  Steven A. Stahl,et al.  Becoming a Fluent Reader: Reading Skill and Prosodic Features in the Oral Reading of Young Readers. , 2004, Journal of educational psychology.

[36]  Jack Mostow,et al.  Mining Data from Project LISTEN's Reading Tutor to Analyze Development of Children's Oral Reading Prosody , 2012, FLAIRS.

[37]  Timothy V. Rasinski,et al.  Training teachers to attend to their students’ oral reading fluency , 1991 .

[38]  Donald L. Compton,et al.  Exploring the Relationship between Text–Leveling Systems and Reading Accuracy and Fluency in Second–Grade Students who Are Average and Poor Decoders , 2004 .

[39]  Timothy V. Rasinski,et al.  Reading Fluency: More Than Automaticity? More Than a Concern for the Primary Grades? , 2009 .

[40]  Satanjeev Banerjee,et al.  Evaluating the effect of predicting oral reading miscues , 2003, INTERSPEECH.

[41]  Richard Sproat,et al.  Multilingual Text-to-Speech Synthesis: The Bell Labs Approach , 1998, CL.

[42]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[43]  Sean Martin,et al.  Analysis and Detection of Reading Miscues for Interactive Literacy Tutors , 2004, COLING.

[44]  Refractor,et al.  Third webspace to thumb digital nerve transfer for traumatic avulsion injury , 2023, The Journal of hand surgery, European volume.

[45]  Jack Mostow,et al.  The Sounds of Silence: Towards Automated Evaluation of Student Learning in a Reading Tutor that Listens , 1997, AAAI/IAAI.

[46]  Abeer Alwan,et al.  Assessment of emerging reading skills in young native speakers and language learners , 2009, Speech Commun..

[47]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[48]  Valerie L. Beattie,et al.  LEARNING READING ASSISTANT TM : CMU SPHINX TECHNOLOGY IN A COMMERCIAL EDUCATIONAL SOFTWARE APPLICATION , 2010 .

[49]  Jan P. H. van Santen,et al.  Segmental Duration and Speech Timing , 1997, Computing Prosody.

[50]  Jack Mostow,et al.  Lessons from Project LISTEN’s Session Browser , 2009 .