Abstract
This chapter describes the first year of research on the effectiveness of automated speech recognition (ASR) for ESL learners in the early school years. The aim was to learn how such technology can enhance literacy learning as an element of L2 development, using prototype research software entitled the Reading Tutor (RT). In addition to assessing learners’ gains in reading scores, the attitudinal dimension of speech recognition technology was investigated in an effort to explain the effectiveness of the software. We found that both heritage language (L1) and level of English proficiency were linked to students’ reading gains with the RT. Further, the RT was shown to be equally effective to a more time-intensive volunteer tutoring program. A positive affective impact of the RT was demonstrated in the interview data but not in two widely used attitudinal scales. An Appendix describes the technical implementation of the project.
Key Terms in this Chapter
Automated Speech Recognition (ASR): A branch of natural language processing in computer science, ASR uses speech processing software to recognize words and in some applications, speech sounds in a speaker’s spoken input. The Reading Tutor’ ASR function is based upon Carnegie Mellon University’s open-source Sphinx-II speech analyzer, which uses the well-known probabilistic “Hidden Markov Model” approach to interpreting spoken input.
Language Proficiency: A measure of level of mastery of a target language by learners. Mastery is often defined in terms of a fairly complex set of skills entailed in communicative competence, and hence can involve more than accuracy of pronunciation, vocabulary or grammar.
Reading Comprehension: A reader’s retrieval of an author’s meaning intentions from a written text. The tests used to assess reading comprehension in the present studies (WRMT-R) evaluate a reader’s understanding of meanings of individual words (“word comprehension”) and of longer passages of text (“passage comprehension.”)
Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA): ANOVA is one of the more powerful statistical methods of testing differences amongst two or more groups’ mean scores against chance. Its power derives mainly from the fact that it takes into account each case’s contribution to a group’s mean score. An ANCOVA has the added possibility of including a correction across differing groups for a potentially-confounding variable, thereby controlling that variable’s impact upon a group difference of interest to the researcher.
Scaffolding of Learning, Emotional Scaffolding: First used by the cognitive psychologist Jerome Bruner, scaffolding of learning refers to the ways in which adults hold aspects of an environment constant, allowing a learner to focus on the element of learning which is being acquired. Such scaffolding can be gradually withdrawn, allowing a learner to operate independently. A frequently-used example is the way in which some story books for children will follow a fixed syntactic pattern from page to page and introduce only one word or phrase on each succeeding page. The term “emotional scaffolding” is used in Project Listen’s Reading Tutor research in a more general sense to refer to the user interface’s provision of emotional support such as praise and encouragement to users during interactions with the program.
Heritage Language: The language that was principally used by an immigrant community prior to their migration to a new linguistic community. Heritage languages are often maintained by vital immigrant communities, and can be promoted by enlightened language policies as a way of enhancing the multilingual nature and cultural diversity of a community.
Affective Dimension, Affective Domain: Part of the most widely cited taxonomy of educational objectives, the affective domain differs from the cognitive domain in learning in that it emphasises feelings and attitudes toward learning and toward the content of learning.