Publication: An attention Long Short-Term Memory based system for automatic classification of speech intelligibility
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2020-11
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Elsevier
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Abstract
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.
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Speech intelligibility, Dysarthria, Long Short-Term Memory (LSTM), Attention model, Machine learning
Bibliographic citation
Fernández-Díaz, M. & Gallardo-Antolín, A. (2020). An attention Long Short-Term Memory based system for automatic classification of speech intelligibility. Engineering Applications of Artificial Intelligence, 96, 103976.