Optimizing DPGMM Clustering in Zero Resource Setting Based on Functional Load
By Bin Wu, Sakriani Watiasri Sakti, Jinsong Zhang, Satoshi Nakamura. 2018
10 citations recorded by the source corpus.
Inspired by infant language acquisition, unsupervised subword discovery of zero-resource languages has gained attention recently. The Dirichlet Process Gaussian Mixture Model (DPGMM) achieves top results evaluated by the ABX discrimination test. However, the DPGMM model is too sensitive to acoustic variation and often produces too many types of subword units and a relatively high-dimensional posteriorgram, which implies high computational cost to perform learning and inference, as well as more tendency to be overfitting. This paper proposes applying functional load to reduce the number of sub-word units from DPGMM. We greedily merge pairs of units with the lowest functional load, causing the least information loss of the language. Results on the Xitsonga corpus with the official setting of Zerospeech 2015 show that we can reduce the number of sub-word units by more than two thirds without hurting the ABX error rate. The number of units is close to that of phonemes in human language.
Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sakriani Watiasri Sakti
- Satoshi Nakamura
- Andros Tjandra
- Haizhou Li
- Berrak Sisman
- Mingyang Zhang
- Xu Tan
- Tao Qin
In Probabilistic & Bayesian Methods
Citations in the corpus
- VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019 (2019, background): “However, the DPGMM model is too sensitive to acoustic variations and often produces too many subword units and a relatively high-dimensional posteriogram, which implies high computational cost for learning and inference as well as more tendencies for overfitting [6].”