Abstract In a companion paper (Carranza et al.) submitted to this conference we discuss the importance of collecting specific L1-L2 speech corpora for the sake of developing effective Computer Assisted Pronunciation Training (CAPT) programs. In this paper we examine this point more deeply by reporting on a study that was aimed at compiling and analysing such a corpus to draw up an inventory of recurrent pronunciation errors to be addressed in a CAPT application that makes use of Automatic Speech Recognition (ASR). In particular we discuss some of the results obtained in the analyses of this corpus and some of the methodological issues we had to deal with. The corpus features 8.9 hours of spontaneous, semi-spontaneous and read speech recorded from 20 Japanese students of Spanish L2. The speech data was segmented and transcribed at the orthographic, canonical-phonemic and narrow-phonetic level using Praat software We report on the analyses of the combined annotations and draw up an inventory of errors that should be addressed in the training. We then consider how ASR can be employed to properly detect these errors. Furthermore, we suggest possible exercises that may be included in the training to improve the errors identified
