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    Wyświetlanie 1-3 z 3
    Tytuł:
    Joint Factor Analysis of Channel Mismatch in Whispering Speaker Verification
    Autorzy:
    Lv, G
    Zhao, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/176713.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    joint factor analysis
    whisper
    speaker verification
    Opis:
    A speaker recognition system based on joint factor analysis (JFA) is proposed to improve whisper- ing speakers’ recognition rate under channel mismatch. The system estimated separately the eigenvoice and the eigenchannel before calculating the corresponding speaker and the channel factors. Finally, a channel-free speaker model was built to describe accurately a speaker using model compensation. The test results from the whispered speech databases obtained under eight different channels showed that the correct recognition rate of a recognition system based on JFA was higher than that of the Gaussian Mixture Model–Universal Background Model. In particular, the recognition rate in cellphone channel tests increased significantly.
    Źródło:
    Archives of Acoustics; 2012, 37, 4; 555-559
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speaker Model Clustering to Construct Background Models for Speaker Verification
    Autorzy:
    Dişken, G.
    Tüfekci, Z.
    Çevik, U.
    Powiązania:
    https://bibliotekanauki.pl/articles/177299.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Gaussian mixture models
    k-means
    imposter models
    speaker clustering
    speaker verification
    Opis:
    Conventional speaker recognition systems use the Universal Background Model (UBM) as an imposter for all speakers. In this paper, speaker models are clustered to obtain better imposter model representations for speaker verification purpose. First, a UBM is trained, and speaker models are adapted from the UBM. Then, the k-means algorithm with the Euclidean distance measure is applied to the speaker models. The speakers are divided into two, three, four, and five clusters. The resulting cluster centers are used as background models of their respective speakers. Experiments showed that the proposed method consistently produced lower Equal Error Rates (EER) than the conventional UBM approach for 3, 10, and 30 seconds long test utterances, and also for channel mismatch conditions. The proposed method is also compared with the i-vector approach. The three-cluster model achieved the best performance with a 12.4% relative EER reduction in average, compared to the i-vector method. Statistical significance of the results are also given.
    Źródło:
    Archives of Acoustics; 2017, 42, 1; 127-135
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    SpeakerNet for Cross-lingual Text-Independent Speaker Verification
    Autorzy:
    Habib, Hafsa
    Tauseef, Huma
    Fahiem, Muhammad Abuzar
    Farhan, Saima
    Usman, Ghousia
    Powiązania:
    https://bibliotekanauki.pl/articles/1953543.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    convolutional neural network
    deep learning
    Siamese network
    speaker verification
    text-independent
    binary operation
    Urdu speaker recognition
    Opis:
    Biometrics provide an alternative to passwords and pins for authentication. The emergence of machine learning algorithms provides an easy and economical solution to authentication problems. The phases of speaker verification protocol are training, enrollment of speakers and evaluation of unknown voice. In this paper, we addressed text independent speaker verification using Siamese convolutional network. Siamese networks are twin networks with shared weights. Feature space can be learnt easily by training these networks even if similar observations are placed in proximity. Extracted features from Siamese then can be classified using difference or correlation measures. We have implemented a customized scoring scheme that utilizes Siamese’ capability of applying distance measures with the convolutional learning. Experiments made on cross language audios of multi-lingual speakers confirm the capability of our architecture to handle gender, age and language independent speaker verification. Moreover, our designed Siamese network, SpeakerNet, provided better results than the existing speaker verification approaches by decreasing the equal error rate to 0.02.
    Źródło:
    Archives of Acoustics; 2020, 45, 4; 573-583
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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