N.Harte, S.Vaseghi, B.Milner ‘Dynamic features for segmental speech recognition’ in proceedings of the International Conference on Spoken Language Processing, Philadelphia, 3-6 Oct 1996, pp 933-936
Speech models and features that emphasise the dynamic aspects of speech can provide improved speech recognition. The cepstral time matrix has been established as a successful method of encoding dynamics. The paper extends this set of dynamic features, considering cepstral time features on both a segmental and subsegmental level. This offers the potential of using a conditional PDF for the state observation within a HMM and incorporating this into the training stage. Methods of linear discriminative analysis are applied to the new feature set to identify the subset of features making the greatest contribution to the task of recognition
Please note: There is a known bug in some browsers that causes an
error when a user tries to view large pdf file within the browser window.
If you receive the message "The file is damaged and could not be
repaired", please try one of the solutions linked below based on the
browser you are using.
Items in TARA are protected by copyright, with all rights reserved, unless otherwise indicated.