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Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice ...
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A Neural Pairwise Ranking Model for Readability Assessment ...
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SMDT: Selective Memory-Augmented Neural Document Translation ...
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CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning ...
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Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks ...
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Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese ...
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Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition ...
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Modeling Intensification for Sign Language Generation: A Computational Approach ...
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A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition ...
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Open Source HamNoSys Parser for Multilingual Sign Language Encoding ...
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A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets ...
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Sign Language Video Retrieval with Free-Form Textual Queries ...
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Sign Language Recognition System using TensorFlow Object Detection API ...
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pNLP-Mixer: an Efficient all-MLP Architecture for Language ...
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Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments ...
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Cross-Lingual Text-to-Speech Using Multi-Task Learning and Speaker Classifier Joint Training ...
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Abstract:
In cross-lingual speech synthesis, the speech in various languages can be synthesized for a monoglot speaker. Normally, only the data of monoglot speakers are available for model training, thus the speaker similarity is relatively low between the synthesized cross-lingual speech and the native language recordings. Based on the multilingual transformer text-to-speech model, this paper studies a multi-task learning framework to improve the cross-lingual speaker similarity. To further improve the speaker similarity, joint training with a speaker classifier is proposed. Here, a scheme similar to parallel scheduled sampling is proposed to train the transformer model efficiently to avoid breaking the parallel training mechanism when introducing joint training. By using multi-task learning and speaker classifier joint training, in subjective and objective evaluations, the cross-lingual speaker similarity can be consistently improved for both the seen and unseen speakers in the training set. ...
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Keyword:
Artificial Intelligence cs.AI; Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://arxiv.org/abs/2201.08124 https://dx.doi.org/10.48550/arxiv.2201.08124
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Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation ...
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Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information ...
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