title: Matrix sentence intelligibility prediction using an automatic speech recognition system.
abstract: OBJECTIVE
  The feasibility of predicting the outcome of the German matrix sentence test for different types of stationary background noise using an automatic speech recognition (ASR) system was studied.
  
  
  METHODS
  Speech reception thresholds (SRT) of 50% intelligibility were predicted in seven noise conditions. The ASR system used Mel-frequency cepstral coefficients as a front-end and employed whole-word Hidden Markov models on the back-end side. The ASR system was trained and tested with noisy matrix sentences on a broad range of signal-to-noise ratios.
  
  
  METHODS
  The ASR-based predictions were compared to data from the literature ( Hochmuth et al, 2015 ) obtained with 10 native German listeners with normal hearing and predictions of the speech intelligibility index (SII).
  
  
  RESULTS
  The ASR-based predictions showed a high and significant correlation (R² = 0.95, p < 0.001) with the empirical data across different noise conditions, outperforming the SII-based predictions which showed no correlation with the empirical data (R² = 0.00, p = 0.987).
  
  
  CONCLUSIONS
  The SRTs for the German matrix test for listeners with normal hearing in different stationary noise conditions could well be predicted based on the acoustical properties of the speech and noise signals. Minimum assumptions were made about human speech processing already incorporated in a reference-free ordinary ASR system.
authors.affiliation: a Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg , Germany.
authors.forename: Marc René
authors.initials: MR
authors.lastname: Schädler
authors.affiliation: a Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg , Germany.
authors.forename: Anna
authors.initials: A
authors.lastname: Warzybok
authors.affiliation: a Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg , Germany.
authors.forename: Sabine
authors.initials: S
authors.lastname: Hochmuth
authors.affiliation: a Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg , Germany.
authors.forename: Birger
authors.initials: B
authors.lastname: Kollmeier
mesh_terms: D000161:Acoustic Stimulation; D000162:Acoustics; D001309:Auditory Threshold; D001331:Automation; D032882:Comprehension; D005240:Feasibility Studies; D006801:Humans; D007802:Language; D009622:Noise / Q000009:adverse effects; D010470:Perceptual Masking; D011237:Predictive Value of Tests; D021641:Recognition, Psychology; D015203:Reproducibility of Results; D013018:Sound Spectrography; D013065:Speech Intelligibility; D013067:Speech Perception; D013069:Speech Reception Threshold Test / Q000379:methods*; D049250:Speech Recognition Software
keywords: ASR; SII; Speech intelligibility predictions; matrix test; speech in noise