Analyzing F0 and vowel formants of Persian based on long-term features Homa Asadi1, Volker Dellwo2 1Department of Linguistics, Alzahra University, Tehran, Iran 2Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland Center for Forensic Phonetics & Acoustics Introduction Aim of study Acoustic correlates of speech which show high between-speaker variabiolty coupled with low-whithin speaker variability play an essential role in reflecting speaker-specific information encoded in human speech. Calculating measures of F0 and formant structures based on long-term features are assumed to be more succesful in FVC. By taking an average over a long stretches of speech, acoustic features responsible for linguistic content cancel out each other so that the remaining acoustic properties in the signal characterise individuals featuers of the speaker (Nolan: 1983, Rose: 2002). Earlier studies claim that long-term formant distributions (LTF) is language-independent. It is not unlikely to obtain different results from the same parameters in different languages, possibly due to the differences in their phonology (Kinoshita:.2001). F0 is langauge-dependent and must be analyzed separately in each language. In this study, we aim to analyze long-term features of F0 and vowel formants of Persian, a language which has rather different phonological system from previous investigated languages. Participants & speech material LTF extraction Results Number of speakers= 12 Age= 22 to 35 years old Gender= male Speakers’ language= Persian (Standard Contemporary Persian) 1296 sentences = 12 speakers * 54 sentences * 2 repetitions Measure Factor tested Test Result LTF3-Mean Speaker LM F(141,142)= 6.594, p<0.0001 LTFF3-SD F(141,142)= 0.574, p<0.0001 LTF0-Mean F(141,142)= 39.41, p<0.0001 Results Conclusions The results of a linear model indicate that the effect of speaker is significant for the mean and standard deviation of LTF3 and for the mean of LTF0. No main effect was obtained for the standard deviation of LTF0. The results of within-speaker variability analysis show that the variability of all LTF measures as a function of repetition is not significant. As for LTF0, within-speaker variability was shown in speakers 3, 4 ,6 ,7 and 8 . In other words, 5 speakers among 12 speakers showed difference on their two recording sessions which is indicative of within-speaker variability in the measures of LTF0. Pairwise correlation tests revealed non-significant relationships between LTF3 and LTF0 which subsequently indicates that they carry different information about speakers’ individual features. The result of LTF analyses replicates previous findings on German and English (Moss: 2010, Gold: 2013) which provides more support for the language-independency of the LTF measures. References Gold, E. and French, J.P. (2011). International practices in forensic speaker comparison. Journal of Speech, Language and the Law, 18, 293-307. Gold, E., French, J.P and Harrison, P. (2013). Examining long-term formant distributions as a discriminant in forensic speaker comparisons under a likelihood ratio framework. Proceedings of Meetings on Acoustics, Montreal, Canada, Vol: 19, 1–87. Jessen, M. (2008). Forensic phonetics. Language and Linguistics Compass, 2, 671–711. Kinoshita, K. (2001). Testing realistic forensic speaker identification in Japanese: A likelihood ratio based approach using formants. Ph.D. dissertation, Australian National University. Moos, A. (2010). Long-term formant distribution as a measure of speaker characteristics in read and spontaneous speech. The Phonetician, 101,7-24. Nolan, F. (1983). The phonetic bases of speaker recognition. Cambridge: Cambridge University Press. Rose, P. (2002). Forensic speaker identification. London & New York: Taylor & Francis.