This paper discusses the challenges posed in creating a Computer-Assisted Pronunciation Training (CAPT) environment for multiple languages. By selecting one language from each of three different language families, we show that a single environment may be tailored to cater for different target languages. We detail the challenges faced during the development of a multimodal CAPT environment comprising a toolkit that manages mobile applications using speech signal processing, visualization, and estimation algorithms. Since the applied underlying mathematical and phonological models, as well as the feedback production algorithms, are based on sound signal processing and modeling rather than on particular languages, the system is language-agnostic and serves as an open toolkit for developing phrasal intonation training exercises for an open selection of languages. However, it was necessary to tailor the CAPT environment to the language-specific particularities in the multilingual setups, especially the additional requirements for adequate and consistent speech evaluation and feedback production. In our work, we describe our response to the challenges in visualizing and segmenting recorded pitch signals and modeling the language melody and rhythm necessary for such a multilingual adaptation, particularly for tonal syllable-timed and mora-timed languages.

In tonal languages, tones are associated with both and phonological and lexical domains. Accurate tone articulation is required in order to convey the correct meaning. Learning tones at both word and phrase levels is often challenging for L2 learners with non-tonal language background, because of possible subtle difference between the close tones. In this paper, we discuss an adoption of StudyIntonation CAPT tools to the case of Vietnamese language being a good example of register tonal language with a complex system of tones comprising such features as tone pitch, its length, contour melody, intensity and phonation. The particular focus of this contribution is to assess the adoption of StudyIntonation course toolkit and its pitch processing and visualization algorithms in order to evaluate how the combined use of audio and visual perception mechanisms supported by StudyIntonation may help learners to improve the accuracy of their pronunciation and intonation with respect to tonal languages.