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In this paper, we propose a closed loop system to improve the performance of single-channel speech separation in a speaker independent scenario. The system is composed of two interconnected blocks: a separation block and a speaker identification block. The improvement is accomplished by incorporating the speaker identities found by the speaker identification block as additional information for the...
In speaker identification, most of the computational processing time is required to calculate the likelihood of the test utterance of the unknown speaker with respect to the speaker models in the database. When number of speakers in the database is in the order of 10,000 or more, then computational complexity becomes very high. In this paper, we propose a Maximum Likelihood Linear Regression (MLLR)...
In this paper the application of Gaussian mixture model (GMM) classifier is investigated as an efficient post-processing method to enhance the performance of GMM-based speaker identification systems; such as Gaussian mixture model universal background model (GMM-UBM) scheme. The proposed classifier presents outstanding performance while its computational complexity is almost negligible compared to...
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