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Acoustic model training, using Kaldi, for automatic whispery speech recognition

Kozierski P., Sadalla T., Drgas Sz., Dąbrowski A., Ziętkiewicz J., Giernacki W.

Annals of Computer Science and Information Systems

2018

Vol. 16

109--114

The article presents research on the automatic whispery speech recognition. The main task was to find dependences between a number of triphone classes (number of leaves in decision tree) and the total number of Gaussian distributions and therefore, to determine optimal values, for which the quality of speech recognition is best. Moreover, it was found, how these dependences differ between normal and whispery speech, what was not done earlier, and this is the innovative part of this work. Based on the performed experiments and obtained results one can say that the number of triphone classes (number of leaves) for whispered speech should be significantly lower than for normal speech.