Resumen

ALBORNOZ, E.M; MILONE, D.H; RUFINER, H.L  y  LOPEZ-COZAR, R. Classification of asr word hypotheses using prosodic information and resampling of training data. Lat. Am. appl. res. [online]. 2013, vol.43, n.3, pp. 213-217. ISSN 1851-8796.

In this work, we propose a novel resampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%

Palabras llave : Automatic Speech Recognition; Resampling Corpus; Support Vector Machines; Hypotheses Classification.