Extraction of Lip features for the Identification of Vowels Utterances using MFCC and Geometrical Aspects
Srikanth G N1, M. K Venkatesha2
1Srikanth G N, Research Scholar, Dept. of Electronics & Inst. Engg, R N S Institute of Technology, Bengaluru, India.
2M. K Venkatesha, Principal, R N S Institute of Technology, Benga-luru, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3978-3983 | Volume-8 Issue-5, January 2020. | Retrieval Number: D4238118419/2020©BEIESP | DOI: 10.35940/ijrte.D4238.018520
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Identification of a person’s speech by his lip movement is a challenging task. Even though many software tools available for recognition of speech to text and vice versa, some of the words uttered may not be accurate as spoken and may vary from person to person because of their pronunciation. In addition, in the noisy environment speech uttered may not perceive effectively hence there lip movement for a given speech varies. Lip reading has added advantages when it augmented with speech recognition, thus increasing the perceived infor-mation. In this paper, the video file of a individual person are converted to frames and extraction of only the lip contour for vowels is done by calculating its area and other geometrical as-pects. Once this is done as a part of testing it is compared with three to four people’s lip contour for vowels for first 20 frames. The parameters such as mean, centroid will remain approximate-ly same for all people irrespective of their lip movement but there is change in major and minor axis and hence area changes con-siderably. In audio domain vowel detection is carried out by ex-tracting unique features of English vowel utterance using Mel Frequency Cepstrum Coefficients (MFCC) and the feature vec-tors that are orthonormalized to compare the normalized vectors with standard database and results are obtained with approxima-tion.
Keywords: MFCC, Orthonormal Vectors, Vowels.
Scope of the Article: Mechanics and Materials Aspects of Advanced Construction Materials.