Gaydecki, Patrick

Marcinek, Lubos

Millman, Rebecca

Stone, Michael

English

The application of speech enhancement algorithms for hearingaids may not always be beneficial to increasing speechintelligibility. Therefore, a prior environment classificationcould be important. However, previous speech intelligibilitymodels do not provide any additional information regardingthe reason for a decrease in speech intelligibility. We proposea unique non-intrusive multi-task transfer learning-basedspeech intelligibility prediction model with sceneryclassification (N-MTTL SI model). The solution combines aMel-spectrogram analysis of the degraded speech signal withtransfer learning and multi-task learning to providesimultaneous speech intelligibility prediction (task 1) andscenery classification of ten real-world noise conditions (task2). The model utilises a pre-trained ResNet architecture as anencoder for feature extraction. The prediction accuracy of theN-MTTL SI model for both tasks is high. Specifically, RMSEof speech intelligibility predictions for seen and unseenconditions is 3.76% and 4.06%. The classification accuracy is98%. In addition, the proposed solution demonstrates thepotential of using pre-trained deep learning models in thedomain of speech intelligibility prediction

The University of Manchester - Institutional Repository

N-MTTL SI Model: Non-Intrusive Multi-Task Transfer Learning-Based Speech Intelligibility Prediction Model with Scenery Classification

https://pure.manchester.ac.uk/ws/files/198749712/marcinek21_interspeech.pdf

N-MTTL SI Model: Non-Intrusive Multi-Task Transfer Learning-Based Speech Intelligibility Prediction Model with Scenery Classification

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The University of Manchester - Institutional Repository