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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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Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers., , , , , , , , , and . LREC, page 2819-2826. European Language Resources Association, (2020)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)Articulatory Feature Extraction Using CTC to Build Articulatory Classifiers Without Forced Frame Alignments for Speech Recognition., , and . INTERSPEECH, page 798-802. 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)Improving Low Resource Code-Switched ASR Using Augmented Code-Switched TTS., , , and . INTERSPEECH, page 4771-4775. ISCA, (2020)Exploiting Monolingual Speech Corpora for Code-Mixed Speech Recognition., , , and . INTERSPEECH, page 2150-2154. ISCA, (2019)Articulatory and Stacked Bottleneck Features for Low Resource Speech Recognition., , , , , , and . INTERSPEECH, page 3202-3206. ISCA, (2018)Gujarati-English Code-Switching Speech Recognition using ensemble prediction of spoken language., , and . CoRR, (2024)Cross-lingual acoustic modeling for Indian languages based on Subspace Gaussian Mixture Models., , , and . NCC, page 1-5. IEEE, (2014)