Automatic Speech Recognition with parallel L1 and L2 acoustic phone models to evaluate /l/ allophony in L2 English speech production

The acoustic and articulatory characteristics of the syllable position lateral allophony in English (clear /l/ in onsets vs. dark /l/ in codas) have been well documented. The present study tests whether speech technology derived methods can be used to evaluate lateral allophony in L2 English production, by combining classic acoustic analyses and automatic speech recognition (ASR). In this study, an ASR system is forced to choose between English and French /l/ acoustic phone models when force-aligning a corpus consisting of read English texts by 43 L2 French learners. The output is correlated with a staple measure for /l/ darkness: the difference between the second and first formants (F2-F1). Results show that segments aligned with the French /l/ acoustic model correspond to "clearer" /l/s (i.e. higher values of F2-F1) suggesting automatic, less time consuming methods of speech processing could be used to identify L1 transfer in L2 production.