An Attention Long Short-Term Memory based system for automatic classification of speech intelligibility
<|reference_start|>An Attention Long Short-Term Memory based system for automatic classification of speech intelligibility: Speech intelligibility can be degraded due to multiple factors, such as noisy environments, technical difficulties or biological conditions. This work is focused on the development of an automatic non-intrusive system for predicting the speech intelligibility level in this latter case. The main contribution of our research on this topic is the use of Long Short-Term Memory (LSTM) networks with log-mel spectrograms as input features for this purpose. In addition, this LSTM-based system is further enhanced by the incorporation of a simple attention mechanism that is able to determine the more relevant frames to this task. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. Results show that the attention LSTM architecture outperforms both, a reference Support Vector Machine (SVM)-based system with hand-crafted features and a LSTM-based system with Mean-Pooling.<|reference_end|>
@article{fernández-díaz2024an,
    title={An Attention Long Short-Term Memory based system for automatic
  classification of speech intelligibility},
    author={Miguel Fern'andez-D'iaz and Ascensi'on Gallardo-Antol'in},
    journal={Miguel Fernandez-Diaz and Ascension Gallardo-Antolin Engineering
  Applications of Artificial Intelligence 96 (2020) 103976},
    year={2024},
    doi={10.1016/j.engappai.2020.103976},
    archivePrefix={arXiv},
    eprint={2402.02850},
    primaryClass={eess.AS cs.LG cs.SD}
}