Automatic speech-based classification of gender, age and accent

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    5 Citations (Scopus)

    Abstract

    This paper presents an automatic speech-based classification scheme to classify speaker characteristics. In the training phase, speech data are grouped into speaker groups according to speakers’ gender, age and accent. Voice features are then extracted to feature vectors which are used to train speaker characteristic models with different techniques which are Vector Quantization, Gaussian Mixture Model and Support Vector Machine. Fusion of classification results from those groups is then performed to obtain final classification results for each characteristic. The Australian National Database of Spoken Language (ANDOSL) corpus was used for evaluation of gender, age and accent classification. Experiments showed high performance for the proposed classification scheme
    Original languageEnglish
    Title of host publication11th International Workshop, PKAW 2010
    EditorsB. H. Kang, D. Richards
    Place of PublicationGermany
    PublisherSpringer
    Pages288-299
    Number of pages12
    ISBN (Print)9783642150364
    DOIs
    Publication statusPublished - 2010
    Event11th International Workshop, PKAW 2010 - Daegu, Korea, Republic of
    Duration: 20 Aug 20103 Sep 2010

    Conference

    Conference11th International Workshop, PKAW 2010
    CountryKorea, Republic of
    CityDaegu
    Period20/08/103/09/10

    Fingerprint

    Vector quantization
    Support vector machines
    Fusion reactions
    Experiments

    Cite this

    Tran, D., Huang, X., & Sharma, D. (2010). Automatic speech-based classification of gender, age and accent. In B. H. Kang, & D. Richards (Eds.), 11th International Workshop, PKAW 2010 (pp. 288-299). Germany: Springer. https://doi.org/10.1007/978-3-642-15037-1_24
    Tran, Dat ; Huang, Xu ; Sharma, Dharmendra. / Automatic speech-based classification of gender, age and accent. 11th International Workshop, PKAW 2010. editor / B. H. Kang ; D. Richards. Germany : Springer, 2010. pp. 288-299
    @inproceedings{3b3185ac7e8a4540a006f8127485918f,
    title = "Automatic speech-based classification of gender, age and accent",
    abstract = "This paper presents an automatic speech-based classification scheme to classify speaker characteristics. In the training phase, speech data are grouped into speaker groups according to speakers’ gender, age and accent. Voice features are then extracted to feature vectors which are used to train speaker characteristic models with different techniques which are Vector Quantization, Gaussian Mixture Model and Support Vector Machine. Fusion of classification results from those groups is then performed to obtain final classification results for each characteristic. The Australian National Database of Spoken Language (ANDOSL) corpus was used for evaluation of gender, age and accent classification. Experiments showed high performance for the proposed classification scheme",
    author = "Dat Tran and Xu Huang and Dharmendra Sharma",
    year = "2010",
    doi = "10.1007/978-3-642-15037-1_24",
    language = "English",
    isbn = "9783642150364",
    pages = "288--299",
    editor = "Kang, {B. H.} and D. Richards",
    booktitle = "11th International Workshop, PKAW 2010",
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    address = "Netherlands",

    }

    Tran, D, Huang, X & Sharma, D 2010, Automatic speech-based classification of gender, age and accent. in BH Kang & D Richards (eds), 11th International Workshop, PKAW 2010. Springer, Germany, pp. 288-299, 11th International Workshop, PKAW 2010, Daegu, Korea, Republic of, 20/08/10. https://doi.org/10.1007/978-3-642-15037-1_24

    Automatic speech-based classification of gender, age and accent. / Tran, Dat; Huang, Xu; Sharma, Dharmendra.

    11th International Workshop, PKAW 2010. ed. / B. H. Kang; D. Richards. Germany : Springer, 2010. p. 288-299.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AB - This paper presents an automatic speech-based classification scheme to classify speaker characteristics. In the training phase, speech data are grouped into speaker groups according to speakers’ gender, age and accent. Voice features are then extracted to feature vectors which are used to train speaker characteristic models with different techniques which are Vector Quantization, Gaussian Mixture Model and Support Vector Machine. Fusion of classification results from those groups is then performed to obtain final classification results for each characteristic. The Australian National Database of Spoken Language (ANDOSL) corpus was used for evaluation of gender, age and accent classification. Experiments showed high performance for the proposed classification scheme

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    Tran D, Huang X, Sharma D. Automatic speech-based classification of gender, age and accent. In Kang BH, Richards D, editors, 11th International Workshop, PKAW 2010. Germany: Springer. 2010. p. 288-299 https://doi.org/10.1007/978-3-642-15037-1_24