Abstract
A series of experiments and model developments were performed to quantitatively describe and predict speech recognition in listeners with normal and impaired hearing in quiet as well as in realistic, fluctuating, and spatially distributed noise environments. On a macroscopic level, classical speech-information-based models such as the Speech Intelligibility Index (SII) yield accurate predictions only for average intelligibility scores and for a limited set of acoustical situations. A binaural extension using a binaural preprocessing model provides surprisingly accurate predictions for a wide range of acoustically complex, spatial situations.
On a microscopic (i.e. phoneme-to-phoneme) scale, the combination of a psychoacoustically and physiologically motivated preprocessing model with a pattern recognition algorithm adopted from automatic speech recognition (ASR) technology allows for a detailed analysis of phoneme confusions and explains the “man-machine-gap” of approx. 12 dB in signal to noise ratio. This finding highlights the superiority of human world-knowledge-driven (top-down) speech pattern recognition in comparison to the training-data-driven (bottom-up) machine learning approaches.
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Acknowledgment
This work was supported by DFG SFB TRR 39, The active auditory system. CEC-Project Hearcom, and the Audiologie-Initiative Niedersachsen.
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Brand, T., Jürgens, T., Beutelmann, R., Meyer, R.M., Kollmeier, B. (2010). Macroscopic and Microscopic Analysis of Speech Recognition in Noise: What Can Be Understood at Which Level?. In: Lopez-Poveda, E., Palmer, A., Meddis, R. (eds) The Neurophysiological Bases of Auditory Perception. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5686-6_39
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DOI: https://doi.org/10.1007/978-1-4419-5686-6_39
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