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

publication date

  • October 2021

start page

  • 49

end page

  • 60


  • 456

International Standard Serial Number (ISSN)

  • 0925-2312

Electronic International Standard Serial Number (EISSN)

  • 1872-8286


  • 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 representations derived from them that discard important temporal information. In the second
    one, two different strategies for the combination of per-frame acoustic log-mel and modulation spectrograms
    into the LSTM framework are explored: at decision level or late fusion and at utterance level or
    Weighted-Pooling (WP) fusion. The proposed models are evaluated with the UA-Speech database that
    contains dysarthric speech with different degrees of severity. On the one hand, results show that attentional
    LSTM networks are able to adequately modeling the modulation spectrograms sequences producing
    similar classification rates as in the case of log-mel spectrograms. On the other hand, both
    combination strategies, late and WP fusion, outperform the single-feature systems, suggesting that
    per-frame log-mel and modulation spectrograms carry complementary information for the task of speech
    intelligibility prediction, than can be effectively exploited by the LSTM-based architectures, being the system
    with the WP fusion strategy and Attention-Pooling the one that achieves best results


  • Telecommunications


  • speech intelligibility; lstm; attention; acoustic spectrogram; modulation spectrogram; fusion