NON-NATIVE SPEECH CORPORA FOR THE DEVELOPMENT OF COMPUTER ASSISTED PRONUNCIATION TRAINING SYSTEMS

In this paper we discuss the need for compiling non-native speech corpora for the development of Computer Assisted Pronunciation Training (CAPT) applications that address specific language pairs. Learner's L1-oriented CAPT tools enhanced with Automatic Speech Recognition (ASR) technology can perform better in automatically identifying common errors of speakers of a specific L1 when learning an L2. Nevertheless, the adaptation of an ASR system to non-native speech is a complex and time-consuming task which demands large quantities of speech data annotated at different transcription levels, and this data is generally not easily available for developers. Background research on CAPT development is presented by reporting on various projects that make use of non-native corpora for developing CAPT applications. We then present two different studies which share the objective of compiling an L1-L2 specific non-native corpus with the purpose of developing a CAPT system for the addressed language pair. The results of the two studies show that specific language combinations trigger specific pronunciations errors and these errors should be conveniently described in order to incorporate this information into the CAPT system and provide students with meaningful and effective feedback on their specific mispronunciations. Procedures for the compilation, annotation and transcription of non-native speech corpora that will improve the reusability and exchange of the databases will be also addressed. We discuss some possibilities for facilitating this task by means of using materials already provided with the orthographic transcription, which can be employed to automatically generate the phonemic transcription, or obtaining transcriptions by other methods such as crowdsourcing. These procedures are less time-consuming and should make it easier to develop effective applications. Enhancing awareness of the need for learner's speech databases is another priority that should be addressed, not only for research purposes, but also for the development of applications.