###### Abstract
However, there are scarce empirical experiments in the state-of-the-art which focus on pronunciation instruction and assessment for native Japanese learners of Spanish as a foreign language, and as far as we are concerned, no one has included ASR technology. For instance, 1440 utterances of Japanese learners of Spanish as a foreign language (A1-A2) were analyzed manually with Praat by phonetics experts in [15]. Students performed different perception and production tasks with an instructor, and they achieved positive significant differences (at the segmental level) between the pre-test and post-test values. A pilot study on the perception of Spanish stress by Japanese learners of Spanish was reported in [16]. Native and non-native participants listened to natural speech recorded by a native Spanish speaker and were asked to mark one of three possibilities (the same word with three stress variants) on an answer sheet. Non-native speech was manually transcribed with Praat by phonetic experts in [17], in an attempt to establish rule-based strategies for labeling intermediate realizations, helping to detect both canonical and erroneous realizations in a potential error detection system. Different perception tasks were carried out in [18]. It was reported how the speakers of native language (L1) Japanese tend to perceive Spanish /y/ when it is pronounced by native speakers of Spanish, and how the L1 Spanish and L1 Japanese listeners evaluate and accept various consonants as allophones of Spanish /y/, comparing both groups. In previous work, we presented the development and the first pilot test of a CAPT application with ASR and text-to-speech (TTS) technology, Japanol, through a training protocol [19, 20]. This learning application for smart devices includes a specific exposure-perception-production cycle of training activities with minimal pairs, which are presented to students in lessons on the most difficult Spanish constructs for native Japanese speakers. We were able to empirically measure a statistically significant improvement between the pre and post-test values of eight native Japanese speakers in a single experimental group. The students' utterances were assessed by experts in phonetics and by the gASR system, obtaining strong correlations between human and machine values. After this first pilot test, we wanted to take a step further and to find pronunciation mistakes associated with key features of the proficiency-level characterization of more participants (33) and different groups (3). However, assessing such a quantity of utterances by human raters can lead to problems regarding time and resources. Furthermore, the gASR pricing policy and its limited black-box functionalities also motivated us to look for alternatives to assess all the utterances, developing a specific ASR system for Spanish from scratch, using Kaldi (kASR). In this work, we analyze the audio utterances of the pre-test and post-test of 33 Japanese learners of Spanish as a foreign language with two different ASR systems (gASR and kASR) to address the research question of how these general and specific-purpose ASR systems compare in the assessment of short isolated words used as challenges in a learning application for CAPT. This paper is organized as follows. The experimental procedure is described in Section 2, which includes the participants and protocol definition, a description of the CAPT tool, a brief description of the process for elaborating the kASR system, and the collection of metrics and instruments for collecting the necessary data. Section 3 presents, on one hand, the results of the training of the users that worked with the CAPT tool and, on the other hand, the performance of the two versions of the ASR systems: the word error rate (WER) values of the kASR system developed, the pronunciation assessment of the participants at the beginning and at the end of the experiment, including intra- and inter-group differences, and the ASR scores' correlation of both ASR systems.