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Overcoming Data Sparsity in Acoustic Modeling of Low-Resource Language by Borrowing Data and Model Parameters from High-Resource Languages.

, , and . INTERSPEECH, page 3037-3041. ISCA, (2016)

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Overcoming Data Sparsity in Acoustic Modeling of Low-Resource Language by Borrowing Data and Model Parameters from High-Resource Languages., , and . INTERSPEECH, page 3037-3041. ISCA, (2016)Joint Estimation of Articulatory Features and Acoustic Models for Low-Resource Languages., , and . INTERSPEECH, page 2153-2157. ISCA, (2017)On Improving Acoustic Models for TORGO Dysarthric Speech Database., , and . INTERSPEECH, page 2695-2699. ISCA, (2017)Articulatory Feature Extraction Using CTC to Build Articulatory Classifiers Without Forced Frame Alignments for Speech Recognition., , and . INTERSPEECH, page 798-802. ISCA, (2016)A data-driven phoneme mapping technique using interpolation vectors of phone-cluster adaptive training., , and . SLT, page 36-41. IEEE, (2014)FMLLR Speaker Normalization With i-Vector: In Pseudo-FMLLR and Distillation Framework., , and . IEEE ACM Trans. Audio Speech Lang. Process., 26 (4): 797-805 (2018)Cross-lingual acoustic modeling for Indian languages based on Subspace Gaussian Mixture Models., , , and . NCC, page 1-5. IEEE, (2014)Generalized Distillation Framework for Speaker Normalization., , , and . INTERSPEECH, page 739-743. ISCA, (2017)DNNs for Unsupervised Extraction of Pseudo FMLLR Features Without Explicit Adaptation Data., , , and . INTERSPEECH, page 3479-3483. ISCA, (2016)