On combining acoustic and modulation spectrograms in an attention LSTM-based system for speech intelligibility level classification

Speech intelligibility can be affected by multiple factors, such as noisy environments, channel distortions or physiological issues. In this work, we deal with the problem of automatic prediction of the speech intelligibility level in this latter case. Starting from our previous work, a non-intrusive system based on LSTM networks with attention mechanism designed for this task, we present two main contributions. In the first one, it is proposed the use of per-frame modulation spectrograms as input features, instead of compact …

attention case classification cs.lg deal eess.as environments lstm multiple prediction speech work