Over the last few years, we have witnessed a growing interest in computer-assisted pronunciation training (CAPT) tools and the commercial success of foreign language teaching applications that incorporate speech synthesis and automatic speech recognition technologies. However, empirical evidence supporting the pedagogical effectiveness of these systems remains scarce. In this study, a minimal-pair based CAPT tool that implements exposure-perception-production cycles and provides automatic feedback to learners is tested for effectiveness in training adult native Spanish users (English level B1-B2) in the production of a set of difficult English sounds. Working under controlled conditions, a group of users took a pronunciation test before and after using the tool. Test results were considered against those of an in-classroom group who followed similar training within the traditional classroom setting. Results show a significant pronunciation improvement among the learners who used the CAPT tool, as well as a correlation between human rater's assessment of post-tests and automatic CAPT assessment of users.

Availability and usability of mobile smart devices and speech technologies ease the development of language learning applications, although many of them do not include pronunciation practice and improvement. A key to success is to choose the correct methodology and provide a sound experimental validation assessment of their pedagogical effectiveness. In this work we present an empirical evaluation of Japañol, an application designed to improve pronunciation of Spanish as a foreign language targeted to Japanese people. A structured sequence of lessons and a quality assessment of pronunciations before and after completion of the activities provide experimental data about learning dynamics and level of improvement. Explanations have been included as corrective feedback, comprising textual and audiovisual material to explain and illustrate the correct articulation of the sounds. Pre-test and post-test utterances were evaluated and scored by native experts and automatic speech recognition, showing a correlation over 0.86 between both predictions. Sounds [s], [fl], [R] and [s], [fR], [T] explain the most frequent perception and production failures, respectively, which can be exploited to plan future versions of the tool, including gamified ones. Final automatic scores provided by the application highly correlate (r>0.91) to expert evaluation and a significant pronunciation improvement can be measured.

Learning games have a remarkable potential for education. They provide an emergent form of social participation that deserves the assessment of their usefulness and efficiency in learning processes. This study describes a novel learning game for foreign pronunciation training in which players can challenge each other. Native Spanish speakers performed several pronunciation activities during a one-month competition using a mobile application, designed under a minimal pairs approach, to improve their pronunciation of English as a foreign language. This game took place in a competitive scenario in which students had to challenge other participants in order to get high scores and climb up a leaderboard. Results show intense practice supported by a significant number of activities and playing regularity, so the most active and motivated players in the competition achieved significant pronunciation improvement results. The integration of automatic speech recognition (ASR) and text-to-speech (TTS) technology allowed users to improve their pronunciation while being immersed in a highly motivational game. INDEX TERMS Computer-assisted pronunciation training, mobile learning game, mobile application, English L2 pronunciation, challenges, motivation.

Feedback is an important concern in Computer-Assisted Pronunciation Training (CAPT), inasmuch as it bears on a system's capability to correct users' input and promote improved L2 pronunciation performance in the target language. In this paper, we test the use of synthetic voice as a corrective feedback resource. A group of students used a CAPT tool for carrying out a battery of minimal-pair discrimination-production tasks; to those who failed in production routines, the system offered the possibility of undergoing extra training by using synthetic voice as a model in a round of exposure exercises. Participants who made use of this resource significantly outperformed those who directly repeated the previously failed exercise. Results suggest that the Text-To-Speech systems offered by current operating systems (Android in our case) must be considered a relevant feedback resource in pronunciation training, especially when combined with efficient teaching methods.

General-purpose automatic speech recognition (ASR) systems have improved in quality and are being used for pronunciation assessment. However, the assessment of isolated short utterances, such as words in minimal pairs for segmental approaches, remains an important challenge, even more so for non-native speakers. In this work, we compare the performance of our own tailored ASR system (kASR) with the one of Google ASR (gASR) for the assessment of Spanish minimal pair words produced by 33 native Japanese speakers in a computer-assisted pronunciation training (CAPT) scenario. Participants in a pre/post-test training experiment spanning four weeks were split into three groups: experimental, in-classroom, and placebo. The experimental group used the CAPT tool described in the paper, which we specially designed for autonomous pronunciation training. A statistically significant improvement for the experimental and in-classroom groups was revealed, and moderate correlation values between gASR and kASR results were obtained, in addition to strong correlations between the post-test scores of both ASR systems and the CAPT application scores found at the final stages of application use. These results suggest that both ASR alternatives are valid for assessing minimal pairs in CAPT tools, in the current configuration. Discussion on possible ways to improve our system and possibilities for future research are included.