Incorporating the Digit Triplet Test in A Lightweight Speech Intelligibility Prediction for Hearing Aids
Xiajie Zhou, Candy Olivia Mawalim, Benita Angela Titalim, Masashi Unoki · 2023
Recent studies in speech processing often utilize sophisticated methods for solving a task to obtain high-accuracy results. Although high performance could be achieved, the methods are too complex and require high-performance computational power that might not be available for a wide range of researchers. In this study, we propose a method to incorporate the low dimensional and the recent state-of-the-art acoustic features for speech processing to predict the speech intelligibility in noise for hearing aids. The proposed method was developed based on the stack regressor on various traditional machine learning regressors. Unlike other existing works, we utilized the results of the digit triplet test, which is usually used to measure the hearing ability in the existence of noise, to improve the prediction. The evaluation of our proposed method was carried out by using the first Clarity Prediction Challenge dataset. This dataset is utilized for speech intelligibility prediction that consists of speech signals output of hearing aids that were arranged in various simulated scenes with interferers. Our experimental results show that the proposed method could improve speech intelligibility prediction. The results also show that the digit triplet test results are beneficial for speech intelligibility prediction in noise.