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Modeling individual vocal differences in group-living lemurs using vocal tract morphology

Current Zoology 2017年第4期63卷 467-475页

作者： Marco GAMBA Livio FAVARO Alessandro ARALDI Valentina MATTEUCCI Cristina GIACOMA Olivier FRIARD Department of Life Sciences and Systems Biology University of Torino Via Accademia Albertina 13 Torino 10134 Italy Zoom Torine S.p.A Strada Piscina 36 Cumiana (Torino) 10040 Italy

Vocal individuality is widespread in social animals. Individual variation in vocalizations is a prereq- uisite for discriminating among conspecifics and may have facilitated the evolution of large complex societies. Ring-tailed lemurs Lemur catta live in relatively large social groups, have con- spicuous vocal repertoires, and their species-specific utterances can be interpreted in light of source-filter theory of vocal production. Indeed, their utterances allow individual discrimination and even recognition thanks to the resonance frequencies of the vocal tract. The purpose of this study is to determine which distinctive vocal features can be derived from the morphology of the upper vocal tract. To accomplish this, we built computational models derived from anatomical measurements collected on lemur cadavers and compared the results with the spectrographic out- put of vocalizations recorded from ex situ live individuals. Our results demonstrate that the mor- phological variation of the ring-tailed lemur vocal tract explains individual distinctiveness of their species-specific utterances. We also provide further evidence that vocal tract modeling is a power- ful tool for studying the vocal output of non-human primates.

关键词： formants indexical cues Lemur catta nasal resonance ring-tailed lemur.

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Classification of Normal and Pathological Voice Using SVM and RBFNN

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Journal of Signal and Information Processing 2014年第1期5卷 1-7页

作者： V. Sellam J. Jagadeesan Department of Computer Science and Engineering SRM University Chennai India

The identification and classification of pathological voice are still a challenging area of research in speech processing. Acoustic features of speech are used mainly to discriminate normal voices from pathological voices. This paper explores and compares various classification models to find the ability of acoustic parameters in differentiating normal voices from pathological voices. An attempt is made to analyze and to discriminate pathological voice from normal voice in children using different classification methods. The classification of pathological voice from normal voice is implemented using Support Vector Machine (SVM) and Radial Basis Functional Neural Network (RBFNN). The normal and pathological voices of children are used to train and test the classifiers. A dataset is constructed by recording speech utterances of a set of Tamil phrases. The speech signal is then analyzed in order to extract the acoustic parameters such as the Signal Energy, pitch, formant frequencies, Mean Square Residual signal, Reflection coefficients, Jitter and Shimmer. In this study various acoustic features are combined to form a feature set, so as to detect voice disorders in children based on which further treatments can be prescribed by a pathologist. Hence, a successful pathological voice classification will enable an automatic non-invasive device to diagnose and analyze the voice of the patient.

关键词： Terms—Pitch formants Jitter Shimmer Reflection Coefficients SVM RBFNN

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