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Gender classification using pitch and formants

Published: 12 February 2011 Publication History

Abstract

A gender classification system is proposed based on pitch, formants and combination of both. Ten Hindi digits database has been prepared for fifty speakers. Each Speaker has spoken each digit ten times. Formants derived from speech samples have been used for gender classification. Gender classification has been also done by using pitch extracted from different methods. Autocorrelation, Cepstrum and Average Magnitude Difference (AMDF) methods have been used for pitch determination from speech samples. Formants in combination with pitch are also used for gender classification. A feature vector consisting of pitches derived from all the above mentioned pitch determination methods was also used for gender classification. Experiments were performed for both open-set and closed-set gender classification. Autocorrelation method performed best for gender classification in open-set. Hybrid method (Autocorrelation +AMDF+ Cepstrum) performed best for gender classification in closed-set.

References

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David Gerhard, November 2003, Pitch Extraction and Fundamental Frequency: History and Current Techniques, Technical Report.
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Goangshiuan S. Ying, Leah H. Jamieson and Carl D. Michell. A probabilistic approach to AMDF pitch detection by School of Electrical and Computer engineering, Purdue University, West Lafayette, (IN 47907-1285). URL: http://purcell.ecn.purdue.edu/~speechg
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A. Michael Noll. 1967. Cepstrum Pitch Determination by, Journal of Acoustic Society of America, Vol. 14 No. 2, pp. 1.
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Zeng Y.-M., Wu Z.-Y., Falk T. & Chan W.-Y. 2006. Robust GMM based gender classification using pitch and RASTA-PLP parameters of speech. Proceedings of 5th Int. Conference on Machine learning and Cybernetics, pp. 3376--3379, Dalian, 2006.
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Margarita Kotti and Constantine Kotropoulos. December 2008. Gender Classification in Two Emotional Speech Databases, IEEE International Conference on Pattern Recognition, pp. 1--4, Dec. 2008.
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B. Gold and N. Morgan. 2003. Speech and audio signal Processing, John Wiley and Sons, 2003.
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L. R. Rabiner and B. H. Juang. 2003. Fundamental of Speech Recognition, 1st ed., Pearson Education, Delhi, 2003.
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  • (2022)A machine learning approach for gender identification using statistical features of pitch in speechesApplied Acoustics10.1016/j.apacoust.2021.108392185(108392)Online publication date: Jan-2022
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    cover image ACM Other conferences
    ICCCS '11: Proceedings of the 2011 International Conference on Communication, Computing & Security
    February 2011
    656 pages
    ISBN:9781450304641
    DOI:10.1145/1947940
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 12 February 2011

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    Author Tags

    1. AMDF
    2. autocorrelation
    3. cepstrum
    4. formants
    5. pitch

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    Cited By

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    • (2025)Partial Fake Speech Attacks in the Real World Using Deepfake AudioJournal of Cybersecurity and Privacy10.3390/jcp50100065:1(6)Online publication date: 8-Feb-2025
    • (2023)Voice Based Gender Detection2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN)10.1109/CICTN57981.2023.10140810(288-291)Online publication date: 20-Apr-2023
    • (2022)A machine learning approach for gender identification using statistical features of pitch in speechesApplied Acoustics10.1016/j.apacoust.2021.108392185(108392)Online publication date: Jan-2022
    • (2022)Contextual Perceptions of Feminine-, Masculine- and Gender-Ambiguous-Sounding Conversational AgentsInformation for a Better World: Shaping the Global Future10.1007/978-3-030-96957-8_38(459-480)Online publication date: 23-Feb-2022
    • (2021)Robust Speech Analysis Framework Using CNN2021 3rd International Conference on Advancements in Computing (ICAC)10.1109/ICAC54203.2021.9671080(485-490)Online publication date: 9-Dec-2021
    • (2020)Voice Gender Recognizer Recognition of Gender from Voice using Deep Neural Networks2020 International Conference on Smart Electronics and Communication (ICOSEC)10.1109/ICOSEC49089.2020.9215254(319-324)Online publication date: Sep-2020
    • (2017)Categorical perception of pitch: Influence of language tone, linguistic meaning, and pitch contourJournal of Indian Speech Language & Hearing Association10.4103/jisha.JISHA_24_1731:2(66)Online publication date: 2017
    • (2016)Phase Based Mel Frequency Cepstral Coefficients for Speaker IdentificationInformation Systems Design and Intelligent Applications10.1007/978-81-322-2757-1_31(309-316)Online publication date: 4-Feb-2016
    • (2015)Gender identification and performance analysis of speech signals2015 Global Conference on Communication Technologies (GCCT)10.1109/GCCT.2015.7342709(483-489)Online publication date: Apr-2015
    • (2015)Show me how you Drive and I’ll Tell you who you are Recognizing Gender Using Automotive Driving ParametersProcedia Manufacturing10.1016/j.promfg.2015.07.7433(5587-5594)Online publication date: 2015
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