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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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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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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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CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement
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Abstract:
Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of speech to selectively suppress background noise and focus on the target speaker. In this paper, we present a novel language-, noise- and speaker-independent AV deep neural network (DNN) architecture, termed CochleaNet, for causal or real-time speech enhancement (SE). The model jointly exploits noisy acoustic cues and noise robust visual cues to focus on the desired speaker and improve speech intelligibility. The proposed SE framework is evaluated using a first of its kind AV binaural speech corpus, ASPIRE, recorded in real noisy environments, including cafeteria and restaurant settings. We demonstrate superior performance of our approach in terms of both objective measures and subjective listening tests, over state-of-the-art SE approaches, including recent DNN based SE models. In addition, our work challenges a popular belief that scarcity of a multi-lingual, large vocabulary AV corpus and a wide variety of noises is a major bottleneck to build robust language, speaker and noise-independent SE systems. We show that a model trained on a synthetic mixture of the benchmark GRID corpus (with 33 speakers and a small English vocabulary) and CHiME 3 noises (comprising bus, pedestrian, cafeteria, and street noises) can generalise well, not only on large vocabulary corpora with a wide variety of speakers and noises, but also on completely unrelated languages such as Mandarin.
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Keyword:
Audio-Visual; Deep learning; Language-independent; Multi-modal Hearing aids; Noise-independent; Real noisy audio-visual corpus; Speaker independent; Speech enhancement; Speech separation
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URL: https://doi.org/10.1016/j.inffus.2020.04.001 http://researchrepository.napier.ac.uk/Output/2692701
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Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
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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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A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks
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A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks ...
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Lip-reading driven deep learning approach for speech enhancement
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In: abs/1808.00046 ; 1 ; 10 (2019)
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A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks
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