Self-conducted speech audiometry using automatic speech recognition: Simulation results for listeners with hearing loss

Speech-in-noise tests are an important tool for assessing hearing impairment, the successful fitting of hearing aids, as well as for research in psychoacoustics. An important drawback of many speech-based tests is the requirement of an expert to be present during the measurement, in order to assess the listener's performance. This drawback may be largely overcome through the use of automatic speech recognition (ASR), which utilizes automatic response logging. However, such an unsupervised system may reduce the accuracy due to the introduction of potential errors. In this study, two different ASR systems are compared for automated testing: A system with a feed-forward deep neural network (DNN) from a previous study (Ooster et al., 2018), as well as a state-of-the-art system utilizing a time-delay neural network (TDNN). The dynamic measurement procedure of the speech intelligibility test was simulated considering the subjects' hearing loss and selecting from real recordings of test participants. The ASR systems' performance is investigated based on responses of 73 listeners, ranging from normal -hearing to severely hearing-impaired as well as read speech from cochlear implant listeners. The feed-forward DNN produced accurate testing results for NH and unaided HI listeners but a decreased measurement accuracy was found in the simulation of the adaptive measurement procedure when considering aided severely HI listeners, recorded in noisy environments with a loudspeaker setup. The TDNN system produces error rates of 0.6% and 3.0% for deletion and insertion errors, respectively. We estimate that the SRT deviation with this system is below 1.38 dB for 95% of the users. This result indicates that a robust unsupervised conduction of the matrix sentence test is possible with a similar accuracy as with a human supervisor even when considering noisy conditions and altered or disordered speech from elderly severely HI listeners and listeners with a CI.