Recognition of the spoken French alphabet using a two-pass dynamic time warp algorithm
Mots-clés :
frequency-domain analysis, spectral analysis, speech recognition, time-domain analysis, isolated word recognition, word classes differentiation, confusable sets members discrimination, within-class tokens separation, temporal regions, spoken French alphabet, two-pass dynamic time warp algorithm, acoustically similar words, within-class words, special recognition approach, highly confusable sets, cepstral regions, greatest between-word variance, modified frame-specific weighting scheme, error rateRésumé
La reconnaissance automatique de mots isolés par anamorphose temporelle (ou DTW) est un outil adéquat lorsque les mots dictés se distinguent facilement les uns des autres. Par contre, lorsque ces mots sont tirés d’une base de données à haut niveau de difficulté, où les mots sont acoustiquement semblables, un algorithme spécifique doit être employé pour faciliter la tâche. L’alphabet français est un vocabulaire qui se compose de lettres similaires que l’on peut regrouper en 6 classes distinctes.Fichiers supplémentaires
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