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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 2 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 3 |
Context-Based Fake News Detection Model Relying on Deep Learning Models
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In: Electronics; Volume 11; Issue 8; Pages: 1255 (2022)
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| 4 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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| 5 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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| 6 |
Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain
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In: Mathematics; Volume 10; Issue 9; Pages: 1428 (2022)
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| 7 |
How Well Do LSTM Language Models Learn Filler-gap Dependencies?
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 8 |
Deep Learning Methods for Human Behavior Recognition
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Lu, Jia. - : Auckland University of Technology, 2021
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| 9 |
Εφαρμογές βαθιάς μάθησης ... : Applications of deep learning ...
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| 10 |
An Auditory Saliency Pooling-Based LSTM Model for Speech Intelligibility Classification
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In: Symmetry ; Volume 13 ; Issue 9 (2021)
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| 11 |
Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis
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In: Biomimetics ; Volume 6 ; Issue 1 (2021)
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| 12 |
Korean Prosody Phrase Boundary Prediction Model for Speech Synthesis Service in Smart Healthcare
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In: Electronics ; Volume 10 ; Issue 19 (2021)
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| 13 |
Dynamic gesture classification of American Sign Language using deep learning
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| 14 |
Neural semantic role labeling with more or less supervision
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Cai, Rui. - : The University of Edinburgh, 2021
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| 15 |
An auditory saliency pooling-based LSTM model for speech intelligibility classification
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Abstract:
This article belongs to the Section Computer and Engineering Science and Symmetry/Asymmetry. ; Speech intelligibility is a crucial element in oral communication that can be influenced by multiple elements, such as noise, channel characteristics, or speech disorders. In this paper, we address the task of speech intelligibility classification (SIC) in this last circumstance. Taking our previous works, a SIC system based on an attentional long short-term memory (LSTM) network, as a starting point, we deal with the problem of the inadequate learning of the attention weights due to training data scarcity. For overcoming this issue, the main contribution of this paper is a novel type of weighted pooling (WP) mechanism, called saliency pooling where the WP weights are not automatically learned during the training process of the network, but are obtained from an external source of information, the Kalinli’s auditory saliency model. In this way, it is intended to take advantage of the apparent symmetry between the human auditory attention mechanism and the attentional models integrated into deep learning networks. The developed systems are assessed on the UA-speech dataset that comprises speech uttered by subjects with several dysarthria levels. Results show that all the systems with saliency pooling significantly outperform a reference support vector machine (SVM)-based system and LSTM-based systems with mean pooling and attention pooling, suggesting that Kalinli’s saliency can be successfully incorporated into the LSTM architecture as an external cue for the estimation of the speech intelligibility level. ; The work leading to these results has been supported by the Spanish Ministry of Economy, Industry and Competitiveness through TEC2017-84395-P (MINECO) and TEC2017-84593-C2-1-R (MINECO) projects (AEI/FEDER, UE), and the Universidad Carlos III de Madrid under Strategic Action 2018/00071/001.
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Keyword:
Attention; Auditory saliency model; LSTM; Saliency; Speech intelligibility; Telecomunicaciones; Weighted pooling
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URL: https://doi.org/10.3390/sym13091728
http://hdl.handle.net/10016/33706
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| 16 |
A study on the impact of neural architectures for Unsupervised Machine Translation
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| 17 |
Structure-(in)dependent Interpretation of Phrases in Humans and LSTMs
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In: Proceedings of the Society for Computation in Linguistics (2021)
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| 18 |
Narrow-band Deep Filtering for Multichannel Speech Enhancement
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In: https://hal.inria.fr/hal-02378413 ; 2020 (2020)
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| 19 |
Place perception from the fusion of different image representation
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In: Li, P, Li, X, Li, X, Pan, H, Khyam, MO, Noor-A-Rahim, M, Ge, SS, (2020). Place perception from the fusion of different image representation. Pattern Recognition, Vol. 110, p. 1-11 http://dx.doi.org/10.1016/j.patcog.2020.107680 (2020)
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| 20 |
Identifying complaints from product reviews: a case study on Hindi
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In: Singh, Raghvendra Pratap, Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2020) Identifying complaints from product reviews: a case study on Hindi. In: 28th Irish Conference on Artificial Intelligence and Cognitive Science, 7-8 Dec 2020, Dublin, Ireland. (2020)
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