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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments ... Mortensen, David R.; Picone, Jordan; Li, Xinjian. - : arXiv, 2021 BASE Show details 2 Differentiable Allophone Graphs for Language-Universal Speech Recognition ... Yan, Brian; Dalmia, Siddharth; Mortensen, David R.. - : arXiv, 2021 BASE Show details 3 Towards Zero-shot Learning for Automatic Phonemic Transcription ... Li, Xinjian; Dalmia, Siddharth; Mortensen, David R.; Li, Juncheng; Black, Alan W; Metze, Florian. - : arXiv, 2020 Abstract: Automatic phonemic transcription tools are useful for low-resource language documentation. However, due to the lack of training sets, only a tiny fraction of languages have phonemic transcription tools. Fortunately, multilingual acoustic modeling provides a solution given limited audio training data. A more challenging problem is to build phonemic transcribers for languages with zero training data. The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes. In this work, we address this problem by adopting the idea of zero-shot learning. Our model is able to recognize unseen phonemes in the target language without any training data. In our model, we decompose phonemes into corresponding articulatory attributes such as vowel and consonant. Instead of predicting phonemes directly, we first predict distributions over articulatory attributes, and then compute phoneme distributions with a customized ... : AAAI 2020 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD URL: https://dx.doi.org/10.48550/arxiv.2002.11781 https://arxiv.org/abs/2002.11781 BASE Hide details 4 Universal Phone Recognition with a Multilingual Allophone System ... Li, Xinjian; Dalmia, Siddharth; Li, Juncheng. - : arXiv, 2020 BASE Show details

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