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Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
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In: ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora ; https://hal.archives-ouvertes.fr/hal-03623351 ; ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora, Jun 2022, Marseille, France ; https://www.clarin.eu/ParlaCLARIN-III (2022)
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Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
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In: Information; Volume 13; Issue 4; Pages: 175 (2022)
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Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts
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In: Sensors; Volume 22; Issue 3; Pages: 852 (2022)
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Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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An Enhanced Neural Word Embedding Model for Transfer Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
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Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
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In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
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Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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Creating multi-scripts sentiment analysis lexicons for Algerian, Moroccan and Tunisian dialects
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In: 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) ; https://hal.archives-ouvertes.fr/hal-03308111 ; 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT), Sep 2021, Copenhagen, Denmark (2021)
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Bilingual English-German word embedding models for scientific text ...
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Bilingual English-German word embedding models for scientific text ...
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以《Cofacts 真的假的》資料庫為基礎建立中文科學假訊息之探勘模型 ; Text Mining Model for Detecting Chinese Fake Scientific Messages based on Cofacts Open Data
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Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
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Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
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Text ranking based on semantic meaning of sentences ; Textrankning baserad på semantisk betydelse hos meningar
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Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese
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In: Applied Sciences ; Volume 11 ; Issue 22 (2021)
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Acoustic Word Embeddings for End-to-End Speech Synthesis
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In: Applied Sciences ; Volume 11 ; Issue 19 (2021)
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Abstract:
The most recent end-to-end speech synthesis systems use phonemes as acoustic input tokens and ignore the information about which word the phonemes come from. However, many words have their specific prosody type, which may significantly affect the naturalness. Prior works have employed pre-trained linguistic word embeddings as TTS system input. However, since linguistic information is not directly relevant to how words are pronounced, TTS quality improvement of these systems is mild. In this paper, we propose a novel and effective way of jointly training acoustic phone and word embeddings for end-to-end TTS systems. Experiments on the LJSpeech dataset show that the acoustic word embeddings dramatically decrease both the training and validation loss in phone-level prosody prediction. Subjective evaluations on naturalness demonstrate that the incorporation of acoustic word embeddings can significantly outperform both pure phone-based system and the TTS system with pre-trained linguistic word embedding.
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Keyword:
acoustic input tokens; naturalness; speech synthesis; word embedding
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URL: https://doi.org/10.3390/app11199010
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