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1
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization ...
Yan, Brian; Zhang, Chunlei; Yu, Meng. - : arXiv, 2021
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2
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation ...
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3
Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition ...
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4
Continual Learning for Monolingual End-to-End Automatic Speech Recognition ...
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5
Assessing Evaluation Metrics for Speech-to-Speech Translation ...
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6
What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis ...
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7
Integrating Categorical Features in End-to-End ASR ...
Huang, Rongqing. - : arXiv, 2021
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8
Oriental Language Recognition (OLR) 2020: Summary and Analysis ...
Li, Jing; Wang, Binling; Zhi, Yiming. - : arXiv, 2021
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9
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ...
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10
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings ...
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11
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study ...
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12
Speech Representations and Phoneme Classification for Preserving the Endangered Language of Ladin ...
Durante, Zane; Mathur, Leena; Ye, Eric. - : arXiv, 2021
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13
Applying Phonological Features in Multilingual Text-To-Speech ...
Abstract: This study investigates whether phonological features can be applied in text-to-speech systems to generate native and non-native speech in English and Mandarin. We present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological features. We tested whether this mapping could lead to the successful generation of native, non-native, and code-switched speech in the two languages. We ran two experiments, one with a small dataset and one with a larger dataset. The results proved that phonological features could be used as a feasible input system, although further investigation is needed to improve model performance. The accented output generated by the TTS models also helps with understanding human second language acquisition processes. ... : demo webpage: https://congzhang365.github.io/feature_tts/ ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.2110.03609
https://arxiv.org/abs/2110.03609
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14
English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System ...
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15
Cross-lingual Low Resource Speaker Adaptation Using Phonological Features ...
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16
Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis ...
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17
Synchronising speech segments with musical beats in Mandarin and English singing ...
Zhang, Cong; Zhu, Jian. - : arXiv, 2021
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18
Arabic Speech Recognition by End-to-End, Modular Systems and Human ...
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19
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates ...
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20
Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance ...
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