The English pronunciation of Arabic speakers: A data-driven approach to segmental error identification

Abstract

The accurate identification of likely segmental pronunciation errors produced by nonnative speakers of English is a longstanding goal in pronunciation teaching. Most lists of pronunciation errors for speakers of a particular first language (L1) are based on the experience of expert linguists or teachers of English as a second language (ESL) and English as a foreign language (EFL). Such lists are useful, but they are also subject to blind spots for less noticeable errors while suggesting that other more noticeable errors are more important. This exploratory study tested whether using a database of read sentences would reveal recurrent errors that had been overlooked by expert opinions. We did a systematic error analysis of advanced L1 Arabic learners of English (n = 4) using L2 Arctic, a publicly available collection of 1,132 phonetically-balanced English sentences read aloud by 24 speakers of six language backgrounds. To test whether the database was useful for pronunciation error identification, we analysed Arabic speakers’ sentence readings (n = 599), which were annotated in Praat for pronunciation deviations from General American English. The findings give an empirically supported description of persistent pronunciation errors for Arabic learners of English. Although necessarily limited in scope, the study demonstrates how similar datasets can be used regardless of the L1 being investigated. The discussion of errors in pronunciation in terms of their functional loads (Brown, 1988) suggests which persistent errors are likely to be important for classroom attention, helping teachers focus their limited classroom time for optimal learning.