To acquire a second language, one must develop an ear and tongue for the correct stress and intonation patterns of that language. In English language teaching, there is an effective method called Jazz Chants for working on the sound system. In this paper, we propose a method for predicting stressed words, which play a crucial role in Jazz Chants. The proposed method is specially designed for stress prediction in Jazz chants. It exploits several sources of information including words, POSs, sentence types, and the constraint on the number of stressed words in a chant text. Experiments show that the proposed method achieves an F-measure of 0.939 and outperforms the other methods implemented for comparison. The proposed method is expected to be useful in supporting non-native teachers of English when they teach chants to students and create chant texts with stress marks from arbitrary texts.

The optimal way to build speech understanding modules depends on the amount of training data available. When only a small amount of training data is available, effective allocation of the data is crucial to preventing overfitting of statistical methods. We have developed a method for allocating a limited amount of training data in accordance with the amount available. Our method exploits rule-based methods for when the amount of data is small, which are included in our speech understanding framework based on multiple model combinations, i.e., multiple automatic speech recognition (ASR) modules and multiple language understanding (LU) modules, and then allocates training data preferentially to the modules that dominate the overall performance of speech understanding. Experimental evaluation showed that our allocation method consistently outperforms baseline methods that use a single ASR module and a single LU module while the amount of training data increases.