PMID- 29474218
OWN - NLM
STAT- MEDLINE
DCOM- 20190325
LR  - 20190325
IS  - 1538-4667 (Electronic)
IS  - 0196-0202 (Linking)
VI  - 39
IP  - 5
DP  - 2018 Sep/Oct
TI  - An Evaluation of Output Signal to Noise Ratio as a Predictor of Cochlear Implant 
      Speech Intelligibility.
PG  - 958-968
LID - 10.1097/AUD.0000000000000556 [doi]
AB  - OBJECTIVES: Cochlear implant (CI) sound processing strategies are usually 
      evaluated in clinical studies involving experienced implant recipients. Metrics 
      which estimate the capacity to perceive speech for a given set of audio and 
      processing conditions provide an alternative means to assess the effectiveness of 
      processing strategies. The aim of this research was to assess the ability of the 
      output signal to noise ratio (OSNR) to accurately predict speech perception. It 
      was hypothesized that compared with the other metrics evaluated in this study (1) 
      OSNR would have equivalent or better accuracy and (2) OSNR would be the most 
      accurate in the presence of variable levels of speech presentation. DESIGN: For 
      the first time, the accuracy of OSNR as a metric which predicts speech 
      intelligibility was compared, in a retrospective study, with that of the input 
      signal to noise ratio (ISNR) and the short-term objective intelligibility (STOI) 
      metric. Because STOI measured audio quality at the input to a CI sound processor, 
      a vocoder was applied to the sound processor output and STOI was also calculated 
      for the reconstructed audio signal (vocoder short-term objective intelligibility 
      [VSTOI] metric). The figures of merit calculated for each metric were Pearson 
      correlation of the metric and a psychometric function fitted to sentence scores 
      at each predictor value (Pearson sigmoidal correlation [PSIG]), epsilon 
      insensitive root mean square error (RMSE*) of the psychometric function and the 
      sentence scores, and the statistical deviance of the fitted curve to the sentence 
      scores (D). Sentence scores were taken from three existing data sets of 
      Australian Sentence Tests in Noise results. The AuSTIN tests were conducted with 
      experienced users of the Nucleus CI system. The score for each sentence was the 
      proportion of morphemes the participant correctly repeated. In data set 1, all 
      sentences were presented at 65 dB sound pressure level (SPL) in the presence of 
      four-talker Babble noise. Each block of sentences used an adaptive procedure, 
      with the speech presented at a fixed level and the ISNR varied. In data set 2, 
      sentences were presented at 65 dB SPL in the presence of stationary speech 
      weighted noise, street-side city noise, and cocktail party noise. An adaptive 
      ISNR procedure was used. In data set 3, sentences were presented at levels 
      ranging from 55 to 89 dB SPL with two automatic gain control configurations and 
      two fixed ISNRs. RESULTS: For data set 1, the ISNR and OSNR were equally most 
      accurate. STOI was significantly different for deviance (p = 0.045) and RMSE* (p 
      < 0.001). VSTOI was significantly different for RMSE* (p < 0.001). For data set 
      2, ISNR and OSNR had an equivalent accuracy which was significantly better than 
      that of STOI for PSIG (p = 0.029) and VSTOI for deviance (p = 0.001), RMSE*, and 
      PSIG (both p < 0.001). For data set 3, OSNR was the most accurate metric and was 
      significantly more accurate than VSTOI for deviance, RMSE*, and PSIG (all p < 
      0.001). ISNR and STOI were unable to predict the sentence scores for this data 
      set. CONCLUSIONS: The study results supported the hypotheses. OSNR was found to 
      have an accuracy equivalent to or better than ISNR, STOI, and VSTOI for tests 
      conducted at a fixed presentation level and variable ISNR. OSNR was a more 
      accurate metric than VSTOI for tests with fixed ISNRs and variable presentation 
      levels. Overall, OSNR was the most accurate metric across the three data sets. 
      OSNR holds promise as a prediction metric which could potentially improve the 
      effectiveness of sound processor research and CI fitting.
FAU - Watkins, Greg D
AU  - Watkins GD
AD  - Faculty of Engineering and Information Technologies, School of Aerospace, 
      Mechanical and Mechatronic Engineering, University of Sydney, Sydney, Australia.
FAU - Swanson, Brett A
AU  - Swanson BA
AD  - Cochlear Limited, Sydney, Australia.
FAU - Suaning, Gregg J
AU  - Suaning GJ
AD  - Faculty of Engineering and Information Technologies, School of Aerospace, 
      Mechanical and Mechatronic Engineering, University of Sydney, Sydney, Australia.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Ear Hear
JT  - Ear and hearing
JID - 8005585
SB  - IM
MH  - *Cochlear Implants
MH  - Datasets as Topic
MH  - Humans
MH  - Perceptual Masking
MH  - Retrospective Studies
MH  - *Signal-To-Noise Ratio
MH  - *Speech Intelligibility
MH  - *Speech Perception
EDAT- 2018/02/24 06:00
MHDA- 2019/03/26 06:00
CRDT- 2018/02/24 06:00
PHST- 2018/02/24 06:00 [pubmed]
PHST- 2019/03/26 06:00 [medline]
PHST- 2018/02/24 06:00 [entrez]
AID - 10.1097/AUD.0000000000000556 [doi]
PST - ppublish
SO  - Ear Hear. 2018 Sep/Oct;39(5):958-968. doi: 10.1097/AUD.0000000000000556.