Some listeners with @entity393 show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with @entity393 . Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the @entity1650 ( @entity1650 ) task and the spectral ripple depth detection ( @entity7126 ) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that @entity7126 at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that @entity7126 at 2.0 cpo combined with @entity1650 and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that @entity7126 and @entity1650 may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes.

Comparing auditory filter bandwidths, spectral ripple modulation detection, XXXX , and speech recognition: Normal and impaired hearing.

@entity393 :: ('MESH:D034381', 'Disease') :: ['hearing loss'] @entity1650 :: ('MESH:D001308', 'Disease') :: ['SRD', 'spectral ripple discrimination'] @entity7126 :: ('MESH:C537501', 'Disease') :: ['SMD']
@entity1650 :: (MESH:D001308,Disease) :: ['spectral ripple discrimination']
