Automating speech reception threshold measurements using automatic speech recognition

The speech reception threshold (SRT) is the noise level at which the speech recognition rate of a test person is 50%. SRT measurement is relevant for patient screening, psychoacoustic research and algorithm development in hearing aids and cochlear implants. In this paper, we report on our efforts to automate SRT measurement using an automatic speech recognizer. During a test, sentences are presented to the test subject at different SNR levels. The person under test repeats the sentence and the keywords it contains are scored by an audiologist. If all keywords are repeated correctly, the sentence is evaluated as correct. The SNR level of each sentence is adjusted based on the previous sentence’s evaluation. Aiming for an objective and repeatable measurement, the audiologist’s assessment is replaced by an automatic speech recognizer’s evaluation. For this purpose, we investigate different finite state transducer structures to model the expected sentences as well as the impact of several speaker adaptation schemes on the keyword detection accuracy. A baseline recognizer using general acoustic models achieves a performance of 88.8% keyword detection rate. Speaker adapted acoustic models improve the performance yielding a keyword detection accuracy of up to 90.7%. Finally, the impact of recognition errors on the estimated SRT value is simulated showing a minimal impact on the SRT measurement process. Based on this analysis, it can be concluded that the proposed automatic evaluation scheme is a viable tool for speech reception threshold measurements.