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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 86 1 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander... In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021) BASE Show details 2 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander... In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021) BASE Show details 3 Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : Springer, 2021 BASE Show details 4 Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts Ahmed, Rami; Gogate, Mandar; Tahir, Ahsen. - : MDPI, 2021 BASE Show details 5 Does semantics aid syntax? An empirical study on named entity recognition and classification Zhong, Xiaoshi; Cambria, Erik; Hussain, Amir. - : Springer, 2021 BASE Show details 6 Arabic question answering system: a survey Azmi, Aqil M.; Cambria, Erik; Hussain, Amir. - : Springer, 2021 BASE Show details 7 Advances in machine translation for sign language: approaches, limitations, and challenges Sabir, Nabeel; Abid, Adnan; Hussain, Amir. - : Springer, 2021 BASE Show details 8 A novel context-aware multimodal framework for persian sentiment analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier, 2021 BASE Show details 9 A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect Adeel, Ahsan; Guellil, Imane; Gogate, Mandar. - : Springer, 2021 BASE Show details 10 Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model Rabhi, Besma; Elbaati, Abdelkarim; Boubaker, Houcine. - : Springer, 2021 BASE Show details 11 A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect Guellil, Imane; Adeel, Ahsan; Azouaou, Faical. - 2021 BASE Show details 12 A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis Dashtipour, Kia; Gogate, Mandar; Cambria, Erik. - : Elsevier BV, 2021 BASE Show details 13 Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts Ahmed, Rami; Gogate, Mandar; Tahir, Ahsen. - : MDPI, 2021 BASE Show details 14 Persian Sentence-level Sentiment Polarity Classification Dashtipour, Kia; Gogate, Mandar; Gelbukh, Alexander. - : IEEE, 2021. : Piscataway, NJ, USA, 2021 BASE Show details 15 Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes Zhong, Xiaoshi; Cambria, Erik; Hussain, Amir. - : Springer, 2020 BASE Show details 16 CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement Gogate, Mandar; Dashtipour, Kia; Adeel, Ahsan; Hussain, Amir. - : Elsevier, 2020 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. Keyword: Audio-Visual; Deep learning; Language-independent; Multi-modal Hearing aids; Noise-independent; Real noisy audio-visual corpus; Speaker independent; Speech enhancement; Speech separation URL: https://doi.org/10.1016/j.inffus.2020.04.001 http://researchrepository.napier.ac.uk/Output/2692701 BASE Hide details 17 Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances Ahmed, Rami; Dashtipour, Kia; Gogate, Mandar. - : Springer, 2020 BASE Show details 18 Persuasive dialogue understanding: The baselines and negative results Chen, Hui; Ghosal, Deepanway; Majumder, Navonil. - : Elsevier, 2020 BASE Show details 19 Deep Neural Network Driven Binaural Audio Visual Speech Separation Gogate, Mandar; Dashtipour, Kia; Bell, Peter. - : Institute of Electrical and Electronics Engineers, 2020 BASE Show details 20 Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System Gogate, Mandar; Dashtipour, Kia; Hussain, Amir. - 2020 BASE Show details Page: 1 2 3 4 5

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