Study of articulators’ contribution and compensation during speech by articulatory speech recognition

In this paper, the contributions of dynamic articulatory information were evaluated by using an articulatory speech recognition system. The Electromagnetic Articulographic dataset is relatively small and hard to be recorded compared with popular speech corpora used for modern speech study. We used articulatory data to study the contribution of each observation channel of vocal tracts in speech recognition by DNN framework. We also analyzed the recognition results of each phoneme according to speech production rules. The contribution rate of each articulator can be considered as the crucial level of each phoneme in speech production. Furthermore, the results indicate that the contribution of each observation point is not relevant to a specific method. The tendency of a contribution of each sensor is identical to the rules of Japanese phonology. In this work, we also evaluated the compensation effect between different channels. We discovered that crucial points are hard to be compensated for compared with non-crucial points. The proposed method can help us identify the crucial points of each phoneme during speech. The results of this paper can contribute to the study of speech production and articulatory-based speech recognition.