Date of Award
Master of Science
Electrical and Computer Engr
Aaron L Robinson
The aim of this study is to develop robust algorithm to automatically detect the Tone and Break Indices(ToBI) from the speech signal and explore their applications. iLAST was introduced to analyze the acoustic and prosodic features to detect the ToBI indices. Both "expert" and "data" driven rules were used to improve the robustness. The integration of multi-scale signal analysis with rule-based classification has helped in robustly identifying tones that can be used in applications, such as identifying Vowel triangle, emotions from speech etc. Empirical analyses using labeled dataset were performed to illustrate the utility of the proposed approach. Further analyses were conducted to identify the inefficiencies with the proposed approach and address those issues through co-analyses of prosodic features in identifying the major contributors to robust detection of ToBI. It was demonstrated that the proposed approach performs robustly and can be used for developing a wide variety of applications.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Kolli, Chandra Sekhar Rao, "Robust Estimation of Tone Break Indices from Speech Signal using Multi-Scale Analysis and their Applications" (2012). Electronic Theses and Dissertations. 546.