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Full-Text Articles in Signal Processing
Stress And Emotion Classification Using Jitter And Shimmer Features, Xi Li, Jidong Tao, Michael T. Johnson, Joseph Soltis, Anne Savage, Kirsten Leong, John D. Newman
Stress And Emotion Classification Using Jitter And Shimmer Features, Xi Li, Jidong Tao, Michael T. Johnson, Joseph Soltis, Anne Savage, Kirsten Leong, John D. Newman
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
In this paper, we evaluate the use of appended jitter and shimmer speech features for the classification of human speaking styles and of animal vocalization arousal levels. Jitter and shimmer features are extracted from the fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency cepstral coefficients (MFCCs) for human speech and Greenwood function cepstral coefficients (GFCCs) for animal vocalizations. Hidden Markov models (HMMs) with Gaussian mixture models (GMMs) state distributions are used for classification. The appended jitter and shimmer features result in an increase in classification accuracy for several illustrative datasets, including the SUSAS dataset for human speaking …
Application Of Speech Recognition To African Elephant (Loxodonta Africana) Vocalizations, Patrick J. Clemins, Michael T. Johnson
Application Of Speech Recognition To African Elephant (Loxodonta Africana) Vocalizations, Patrick J. Clemins, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
This paper presents a novel application of speech processing research, classification of African elephant vocalizations. Speaker identification and call classification experiments are performed on data collected from captive African elephants in a naturalistic environment. The features used for classification are 12 mel-frequency cepstral coefficients plus log energy computed using a shifted filter bank to emphasize the infrasound range of the frequency spectrum used by African elephants. Initial classification accuracies of 83.8% for call classification and 88.1% for speaker identification were obtained. The long-term goal of this research is to develop a universal analysis framework and robust feature set for animal …