Abstract
Speaker diarization is the process of partitioning an input audio stream into homogeneous segments according to different speakers. It is an important process to support speaker recognition systems and identify a speaker in broadcasts, meeting recordings, and voice mail. Especially it is a fundamental step of automatic checklist reading evaluation system in operating rooms. In this study, we introduce an approach for speaker diarization in Vietnamese voices. The proposed method consists of vectorizing voice based on x-vector and then clustering by mean-shift, k-means, and agglomerative hierarchical techniques to identify speakers in audio. This method attained the accuracy of 89.29% on the 2-speaker mock dialogue generated from the testing set of the VIVOS Corpus dataset.
Keywords
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Nam, N.D., Huynh, H.T. (2021). Speaker Diarization in Vietnamese Voice. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_31
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DOI: https://doi.org/10.1007/978-981-16-8062-5_31
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