Candy Olivia Mawalim, Benita Angela Titalim, Matashi Unoki, Shogo Okada
Thursday, December 15th, 2022, 2pm – 2.30pm
Speech intelligibility prediction for both normal hearing and hearing impairment is very important for hearing aid development. The Clarity Prediction Challenge 2022 (CPC1) was initiated to evaluate the speech intelligibility of speech signals produced by hearing aid systems. Modified binaural short-time objective intelligibility (MBSTOI) and hearing aid speech prediction index (HASPI) were introduced in the CPC1 to understand the basis of speech intelligibility prediction. This paper proposes a method to predict speech intelligibility scores, namely OBISHI. OBISHI is an intrusive (non-blind) objective measurement that receives binaural speech input and considers the hearing-impaired characteristics. In addition, a pre-trained automatic speech recognition (ASR) system was also utilized to infer the difficulty of utterances regardless of the hearing loss condition. We also integrated the hearing loss model by the Cambridge auditory group and the Gammatone Filterbank-based prediction model. The total evaluation was conducted by comparing the predicted intelligibility score of the baseline MBSTOI and HASPI with the actual correctness of listening tests. In general, the results showed that the proposed method, OBISHI, outperformed the baseline MBSTOI and HASPI (improved approximately 10% classification accuracy in terms of F1 score).