In recent years the application of computer software to the learning process has been found to be an indisputably effective tool supporting the traditional teaching methods. Particular focus has been put on the application of techniques based on speech and language processing to the second language learning. Most of the commercial self-study programs, however, do not allow for introduction of an individualized learning course by the teacher and to concentrate on segmental features only. The paper discusses the use of speech technology in the training of foreign languages' pronunciation and prosody and defines pedagogical requirements for an effective training with CAPT systems. In this context, steps taken in the development of the intelligent tutoring system AzAR3.0 (German ‘Automat for accent reduction’) in the scope of the Euronounce project (Cylwik et al., 2008) are described with the focus on creation of the linguistic content. In response to the European Union's call for promoting less widely spoken languages, the project focuses on German as a target language for native speakers of Polish, Slovak, Czech, and Russian, and vice versa. The paper presents the design of the speech corpus for the purpose of the tutoring system and the analysis of pronunciation errors. The results of the latter provide information which is important for Automatic Speech Recognition (ASR) training on the one hand, and for automatic error detection and feedback generation on the other hand. In the end, Pitch Line software for implementation in the prosody visualization and training module of AzAR3.0 tutoring system is described.

Laughter is one of the most important paralinguistic events, and it has specific roles in human conversation. The automatic detection of laughter occurrences in human speech can aid automatic speech recognition systems as well as some paralinguistic tasks such as emotion detection. In this study we apply Deep Neural Networks (DNN) for laughter detection, as this technology is nowadays considered state-of-the-art in similar tasks like phoneme identification. We carry out our experiments using two corpora containing spontaneous speech in two languages (Hungarian and English). Also, as we find it reasonable that not all frequency regions are required for efficient laughter detection, we will perform feature selection to find the sufficient feature subset.