Abstract
| Original language | English |
|---|---|
| Publication date | 2016 |
| Publication status | Published - 2016 |
| Event | ARCHES/ICANHEAR 2016: Audiological Research Cores in Europe (ARCHES) meeting and Improved Communication through Applied Hearing Research (ICanHear) conference - University of Zurich, Zurich, Switzerland Duration: 21 Nov 2016 → 23 Nov 2016 http://www.uzh.ch/orl/events/Arches2016/Program2016/program2016.html |
Conference
| Conference | ARCHES/ICANHEAR 2016 |
|---|---|
| Location | University of Zurich |
| Country | Switzerland |
| City | Zurich |
| Period | 21/11/2016 → 23/11/2016 |
| Internet address |
Cite this
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A correlation metric in the envelope power spectrum domain for speech intelligibility prediction. / Relaño-Iborra, H.; May, Tobias; Zaar, Johannes; Scheidiger, Christoph; Dau, Torsten.
2016. Poster session presented at ARCHES/ICANHEAR 2016, Zurich, Switzerland.Research output: Contribution to conference › Poster › Research › peer-review
TY - CONF
T1 - A correlation metric in the envelope power spectrum domain for speech intelligibility prediction
AU - Relaño-Iborra, H.
AU - May, Tobias
AU - Zaar, Johannes
AU - Scheidiger, Christoph
AU - Dau, Torsten
PY - 2016
Y1 - 2016
N2 - A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speech-based 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 speech-based 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 -