@techreport{oai:ipsj.ixsq.nii.ac.jp:00192701,
 author = {Bin, Wu and Sakriani, Sakti and Jinsong, Zhang and Satoshi, Nakamura and Bin, Wu and Sakriani, Sakti and Jinsong, Zhang and Satoshi, Nakamura},
 issue = {4},
 month = {Dec},
 note = {Unsupervised sub-word discovery of the zero resource language gains attention recently. One of the methods to tackle the problem is using an unsupervised clustering algorithm to recover the discrete phone-like units from the speech, such as the Dirichlet Process Gaussian Mixture Model (DPGMM), which currently achieves top results in the Zero Resource Speech Challenge. However, the DPGMM model is too sensitive to the acoustic variation and often produces too many types of sub-word units. This paper proposes to apply functional load to reduce the size of sub-word units from DPGMM. The functional load is the measurement of how much information in communication is conveyed by contrasts of these units. Then, the aim is to ignore the contrasts of the sub-word units that contribute little in conveying the information of the speech leading to decrease of the number of sub-word classes. We experiment on the official Zerospeech 2015 measuring with ABX error rate., Unsupervised sub-word discovery of the zero resource language gains attention recently. One of the methods to tackle the problem is using an unsupervised clustering algorithm to recover the discrete phone-like units from the speech, such as the Dirichlet Process Gaussian Mixture Model (DPGMM), which currently achieves top results in the Zero Resource Speech Challenge. However, the DPGMM model is too sensitive to the acoustic variation and often produces too many types of sub-word units. This paper proposes to apply functional load to reduce the size of sub-word units from DPGMM. The functional load is the measurement of how much information in communication is conveyed by contrasts of these units. Then, the aim is to ignore the contrasts of the sub-word units that contribute little in conveying the information of the speech leading to decrease of the number of sub-word classes. We experiment on the official Zerospeech 2015 measuring with ABX error rate.},
 title = {Using Functional Load for Optimizing DPGMM based Zero Resource Sub-word Unit Discovery},
 year = {2018}
}