No abstract

(2006) Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags. Journal of the Acoustical Society of America, 120 (6). pp. [4080][4081][4082][4083][4084][4085][4086][4087][4088][4089] This version is available from Sussex Research Online: http://sro.sussex.ac.uk/756/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Bouts of vocalizations given by seven red deer stags were recorded over the rutting period, and homomorphic analysis and hidden Markov models ͑two techniques typically used for the automatic recognition of human speech utterances͒ were used to investigate whether the spectral envelope of the calls was individually distinctive. Bouts of common roars ͑the most common call type͒ were highly individually distinctive, with an average recognition percentage of 93.5%. A "temporal" split-sample approach indicated that although in most individuals these identity cues held over the rutting period, the ability of the models trained with the bouts of roars recorded early in the rut to correctly classify later vocalizations decreased as the recording date increased. When Markov models trained using the bouts of common roars were used to classify other call types according to their individual membership, the classification results indicated that the cues to identity contained in the common roars were also present in the other call types. This is the first demonstration in mammals other than primates that individuals have vocal cues to identity that are common to the different call types that compose their vocal repertoire. Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags

This paper deals with an approach to Automatic Language Identification based on rhythmic modelling. Beside phonetics and phonotactics, rhythm is actually one of the most promising features to be considered for language identification, even if its extraction and modelling are not a straightforward issue. Actually, one of the main problems to address is what to model. In this paper, an algorithm of rhythm extraction is described: using a vowel detection algorithm, rhythmic units related to syllables are segmented. Several parameters are extracted (consonantal and vowel duration, cluster complexity) and modelled with a Gaussian Mixture. Experiments are performed on read speech for 7 languages (English, French, German, Italian, Japanese, Mandarin and Spanish) and results reach up to 86 ± 6% of correct discrimination between stress-timed mora-timed and syllable-timed classes of languages, and to 67 ± 8% percent of correct language identification on average for the 7 languages with utterances of 21 seconds. These results are commented and compared with those obtained with a standard acoustic Gaussian mixture modelling approach (88 ± 5% of correct identification for the 7-languages identification task).