Date of Award
Fall 2006
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Johnson, Michael T.
Second Advisor
Povinelli, Richard
Third Advisor
Struble, Craig
Abstract
Automatic Speech Recognition (ASR) is a useful tool that can facilitate the research and study of animal vocalizations. The use of human speech-based signal processing techniques for animal vocalizations has several pitfalls. Animal vocalizations may not share the same spectral or temporal characteristics as human speech. As a result, the typical ASR assumptions concerning the best frame length, frame overlap and HMM topology may not be suitable for various animal vocalizations. This paper proposes a technique for estimating the frame length, frame overlap and HMM topology from a single, clean, example animal vocalization. Multiple trials are run using the proposed technique, against the vocalizations of two distinct animal species: the Norwegian Ortolan Bunting (Emberiza Hortulana) and the African Elephant (Loxodonta Africana). The results are examined, and the technique provides reasonable estimates for the frame length, the frame overlap and the HMM topology, given the quality of the example vocalizations. Specific recommendations are made for the continuation of this research into a usable tool for animal researches.
Recommended Citation
Ricke, Anthony D., "Automatic Frame Length, Frame Overlap and Hidden Markov Model Topology for Speech Recognition of Animal Vocalizations" (2006). Master's Theses (1922-2009) Access restricted to Marquette Campus. 1314.
https://epublications.marquette.edu/theses/1314