In this study, we present a novel Map and Relabel (MaR) approach that can train ASR systems for new languages with only a few hundred labelled utterances. This ...
Abstract—Modern automatic speech recognition (ASR) system- s require large amounts of data to train the acoustic model, especially with the state-of-the-art ...
This study presents a novel Map and Relabel (MaR) approach that can train ASR systems for new languages with only a few hundred labelled utterances, ...
They firstly employed the transfer learning method to convert a Chinese model into a Uyghur model, by fine-tuning with a small amount of annotated Uyghur data.
Map and relabel: Towards almost-zero resource speech recognition. Y Shi, Z Tang, L Lit, Z Zhang, D Wang. 2018 Asia-Pacific Signal and Information Processing ...
Oct 30, 2018 · Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text ...
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Dec 24, 2024 · In this paper, we study the capacity of a multilingual LLM to perform zero-resource ST and ASR on languages for which we use no labeled audio ...
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Wang, “Map and Relabel: Towards Almost-. Zero Resource Speech Recognition,” 2018 Asia-Pacific Signal Inf. Process. Assoc. Annu. Summit Conf. APSIPA ASC 2018 ...
In this paper, we review the research literature to identify models and ideas that could lead to fully unsupervised ASR.
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Map and Relabel: Towards Almost-Zero Resource Speech Recognition · Computer Science, Linguistics. 2018 Asia-Pacific Signal and Information… · 2018.