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Persian Sentence-level Sentiment Polarity Classification
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In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021)
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Persian Sentence-level Sentiment Polarity Classification
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In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021)
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Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis
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Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
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Does semantics aid syntax? An empirical study on named entity recognition and classification
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Advances in machine translation for sign language: approaches, limitations, and challenges
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A novel context-aware multimodal framework for persian sentiment analysis
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A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
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Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model
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A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect
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A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
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Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
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Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes
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CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement
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Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
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Persuasive dialogue understanding: The baselines and negative results
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Deep Neural Network Driven Binaural Audio Visual Speech Separation
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Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System
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Abstract:
In this paper, we present VIsual Speech In real nOisy eNvironments (VISION), a first of its kind audio-visual (AV) corpus comprising 2500 utterances from 209 speakers, recorded in real noisy environments including social gatherings, streets, cafeterias and restaurants. While a number of speech enhancement frameworks have been proposed in the literature that exploit AV cues, there are no visual speech corpora recorded in real environments with a sufficient variety of speakers, to enable evaluation of AV frameworks' generalisation capability in a wide range of background visual and acoustic noises. The main purpose of our AV corpus is to foster research in the area of AV signal processing and to provide a benchmark corpus that can be used for reliable evaluation of AV speech enhancement systems in everyday noisy settings. In addition, we present a baseline deep neural network (DNN) based spectral mask estimation model for speech enhancement. Comparative simulation results with subjective listening tests demonstrate significant performance improvement of the baseline DNN compared to state-of-the-art speech enhancement approaches.
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URL: https://doi.org/10.21437/interspeech.2020-2935
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