WCSE

Text Independent Speaker Identification for Myanmar Speech

Abstract— Nowadays, speech signal processing is one of the emerging applicat ion areas of digital processing. There are many research areas related to speech processing such as speaker recognition, speech recognition, and speech synthesis. Speaker identificat ion is the task of analy zing the speakers’ characteristics in speech to exactly identify individuals. The identification task performs better when there is enough background training data. Mel Frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP) and Filter Bank features are extracted as front-end processing. Constructing Universal Background Model (UBM) is the main component of i-vector system as it is essential for collecting statistics from speech utterances and for clustering the speaker models. This paper indicates that the impacts of unlimited speech data in speaker identification by using i-vector method with probabilistic linear discriminative analysis (PLDA) approach and the import role of speaker models in identification process.

Index Terms— Speaker Identification (SI), Universal Background Model (UBM), MFCC, Filter Bank, PLP, ivectors, Speaker Model, PLDA, Myanmar Speech

Win Lai Lai Phyu, Win Pa Pa
Natural Language Processing Lab., University of Computer Studies, MYANMAR

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Cite: Win Lai Lai Phyu, Win Pa Pa, "Text Independent Speaker Identification for Myanmar Speech," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering WCSE_2019_SPRING, pp. 86-89, Yangon, Myanmar, February 27-March 1, 2019.