Bounded Generalized Gaussian Mixture Model with ICA

摘要

In this paper, we propose bounded generalized Gaussian mixture model with independent component analysis (ICA). One limitation in ICA is that it assumes the sources to be independent from each other. This assumption can be relaxed by employing a mixture model. In our proposed model, bounded generalized Gaussian distribution (BGGD) is adopted for modeling the data and we have further extended its mixture as an ICA mixture model by employing gradient ascent along with expectation maximization for parameter estimation. By inferring the shape parameter in BGGD, Gaussian distribution and Laplace distribution can be characterized as special cases. In order to validate the effectiveness of this algorithm, experiments are performed on blind source separation (BSS) and BSS as preprocessing to unsupervised keyword spotting. For BSS, TIMIT, TSP and Noizeus speech corpora are selected and results are compared with ICA. For keyword spotting, TIMIT speech corpus is selected and recognition results are further compared before and after BSS being applied as preprocessing when speech utterances are affected by mixing of noise or other speech utterances. The mixing of noise or speech utterances with a particular or target speech utterance can greatly affect the intelligibility of a speech signal. The results achieved from the presented experiments on different applications have demonstrated the effectiveness of ICA mixture model in statistical learning.