Abstract
Objective analysis of intelligibility by a speech recognizer and prosodic features was performed for close-talking recordings before. This study examined whether this is also possible for reverberated speech. In order to ensure that only the room acoustics are different, artificial reverberation was used. 82 patients after partial laryngectomy read a standardized text, 5 experienced raters assessed intelligibility perceptually on a 5-point scale. The best feature subset, determined by Support Vector Regression, consists of the word correctness of a speech recognizer, the average duration of silent pauses, the standard deviation of the \(F_0\) on the entire sample, the standard deviation of jitter, and the ratio of the durations of the voiced sections and the entire recording. A human-machine correlation of r = 0.80 was achieved for the close-talking recordings and r = 0.72 for the worst case of the examined signal qualities. By adding three more features, also r = 0.80 was reached for the reverberated scenario.
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Acknowledgments
We would like to thank Dr. Wolfgang Herbordt for his kind support with the software and data for artificial reverberation. Dr. Döllinger’s contribution was supported by Deutsche Krebshilfe grant no. 111332.
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Haderlein, T., Döllinger, M., Schützenberger, A., Nöth, E. (2016). Influence of Reverberation on Automatic Evaluation of Intelligibility with Prosodic Features. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_53
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