Abstract
In this work we present an approach for text-independent speaker recognition. As features we used Mel Frequency Cepstrum Coefficients (MFCCs) and Temporal Patterns (TRAPs). For each speaker we trained Gaussian Mixture Models (GMMs) with different numbers of densities. The used database was a 36 speakers database with very noisy close-talking recordings. For the training a Universal Background Model (UBM) is built by the EM-Algorithm and all available training data. This UBM is then used to create speaker-dependent models for each speaker. This can be done in two ways: Taking the UBM as an initial model for EM-Training or Maximum-A-Posteriori (MAP) adaptation. For the 36 speaker database the use of TRAPs instead of MFCCs leads to a frame-wise recognition improvement of 12.0 %. The adaptation with MAP enhanced the recognition rate by another 14.2 %.
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© 2007 Springer-Verlag Berlin Heidelberg
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Bocklet, T., Maier, A., Nöth, E. (2007). Text-Independent Speaker Identification Using Temporal Patterns. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_42
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DOI: https://doi.org/10.1007/978-3-540-74628-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74627-0
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