Title

Automatic Frame Length, Frame Overlap and Hidden Markov Model Topology for Speech Recognition of Animal Vocalizations

Grant Title

Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations

Document Type

Dissertation/Thesis

Publication Date

12-2006

Source Publication

Automatic Frame Length, Frame Overlap and Hidden Markov Model Topology for Speech Recognition of Animal Vocalizations

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.

Document Rights and Citation of Original

Ricke, Anthony D. Automatic Frame Length, Frame Overlap and Hidden Markov Model Topology for Speech Recognition of Animal Vocalizations. Unpublished thesis: Marquette University, 2006. © Anthony D. Ricke 2006.

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