This contribution faces the ToBI accent recognition problem with the goal of multiclass identification vs. the more conservative Accent vs. No Accent approach. A neural network and a decision tree are used for automatic recognition of the ToBI accents in the Boston Radio Corpus. Multiclass classification results show the difficulty of the problem and the impact of imbalanced classes. A study of the confusion/similarity between accent types, based on in-pair recognition rates, shows its impact on the overall performance. More expressive F0 contours parametrization techniques have been used to improve recognition rates.
Bibliographic reference. González-Ferreras, César / Vivaracho-Pascual, Carlos / Escudero-Mancebo, David / Cardeñoso-Payo, Valentín (2010): "On the automatic toBI accent type identification from data", In INTERSPEECH-2010, 142-145.