Abstract
| Original language | English |
|---|---|
| Publication date | 2017 |
| Number of pages | 1 |
| Publication status | Published - 2017 |
| Event | 40th MidWinter Meeting of the Association for Research in Otolaryngology - Baltimore Marriott Waterfront , Baltimore, United States Duration: 11 Feb 2017 → 15 Feb 2017 |
Conference
| Conference | 40th MidWinter Meeting of the Association for Research in Otolaryngology |
|---|---|
| Location | Baltimore Marriott Waterfront |
| Country | United States |
| City | Baltimore |
| Period | 11/02/2017 → 15/02/2017 |
Cite this
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Prediction of speech intelligibility based on a correlation metric in the envelope power spectrum domain. / Relano-Iborra, Helia; May, Tobias; Zaar, Johannes; Scheidiger, Christoph; Dau, Torsten.
2017. Poster session presented at 40th MidWinter Meeting of the Association for Research in Otolaryngology, Baltimore, United States.Research output: Contribution to conference › Poster › Research
TY - CONF
T1 - Prediction of speech intelligibility based on a correlation metric in the envelope power spectrum domain
AU - Relano-Iborra, Helia
AU - May, Tobias
AU - Zaar, Johannes
AU - Scheidiger, Christoph
AU - Dau, Torsten
PY - 2017
Y1 - 2017
N2 - A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speechbased envelope power spectrum model (sEPSMcorr) [1], based on the auditory processing of the multi-resolution speech-based Envelope Power Spectrum Model (mr-sEPSM) [2], combined with the correlation back-end of the Short-Time Objective Intelligibility measure (STOI) [3]. The sEPSMcorr can accurately predict NH data for a broad range of listening conditions, e.g., additive noise, phase jitter and ideal binary mask processing.
AB - A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speechbased envelope power spectrum model (sEPSMcorr) [1], based on the auditory processing of the multi-resolution speech-based Envelope Power Spectrum Model (mr-sEPSM) [2], combined with the correlation back-end of the Short-Time Objective Intelligibility measure (STOI) [3]. The sEPSMcorr can accurately predict NH data for a broad range of listening conditions, e.g., additive noise, phase jitter and ideal binary mask processing.
M3 - Poster
ER -