Titlebook: Statistical Language and Speech Processing; 8th International Co Luis Espinosa-Anke,Carlos Martín-Vide,Irena Spasić Conference proceedings

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A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verificationer verification. We try to fill this gap and compare several metric learning loss functions in a systematic manner on the VoxCeleb dataset. The first family of loss functions is derived from the cross entropy loss (usually used for supervised classification) and includes the congenerous cosine loss,

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ANN-MLP Classifier of Native and Nonnative Speakers Using Speech Rhythm Cues corpus. Nonnative speakers (14) are English participants who read the same set of Arabic text then their Arabic counterpart (15). Seven rhythm metrics from all vowels and consonants were calculated from 145 sentences using two rhythm models: Interval Measures (IM) and Compensation/Control Index (CC

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Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systemsconsistently improves the diversity of the generated queries without compromising their quality. We also demonstrate the effectiveness of our generation method as a data augmentation technique for language modelling tasks.

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Exploring Parameter Sharing Techniques for Cross-Lingual and Cross-Task Supervision NLP tasks (dependency parsing, language modeling, named entity recognition and part-of-speech tagging). We conclude that the proposed techniques significantly improve the performance for zero-shot learning.

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ANN-MLP Classifier of Native and Nonnative Speakers Using Speech Rhythm CuesI). Rhythm data were use as input vector of ANN-MLP classifier. The classifier was trained and tested using different configurations of the input vectors. The best accuracy of the engine achieved (80.7%) when we used all speech rhythm input vectors.