Measuring Speech Recognition With a Matrix Test Using Synthetic Speech

Abstract

Speech audiometry is an essential part of audiological diagnostics and clinical measurements. Development times of speech recognition tests are rather long, depending on the size of speech corpus and optimization necessity. The aim of this study was to examine whether this development effort could be reduced by using synthetic speech in speech audiometry, especially in a matrix test for speech recognition. For this purpose, the speech material of the German matrix test was replicated using a preselected commercial system to generate the synthetic speech files. In contrast to the conventional matrix test, no level adjustments or optimization tests were performed while producing the synthetic speech material. Evaluation measurements were conducted by presenting both versions of the German matrix test (with natural or synthetic speech), alternately and at three different signal-to-noise ratios, to 48 young, normal-hearing participants. Psychometric functions were fitted to the empirical data. Speech recognition thresholds were 0.5\u2009dB signal-to-noise ratio higher (worse) for the synthetic speech, while slopes were equal for both speech types. Nevertheless, speech recognition scores were comparable with the literature and the threshold difference lay within the same range as recordings of two different natural speakers. Although no optimization was applied, the synthetic-speech signals led to equivalent recognition of the different test lists and word categories. The outcomes of this study indicate that the application of synthetic speech in speech recognition tests could considerably reduce the development costs and evaluation time. This offers the opportunity to increase the speech corpus for speech recognition tests with acceptable effort.