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Lombard Effect for Bilingual Speakers in Cantonese and English: importance of spectro-temporal features ...
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Speech Audiometry at Home: Automated Listening Tests via Smart Speakers With Normal-Hearing and Hearing-Impaired Listeners
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In: Trends Hear (2020)
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Abstract:
Speech audiometry in noise based on sentence tests is an important diagnostic tool to assess listeners’ speech recognition threshold (SRT), i.e., the signal-to-noise ratio corresponding to 50% intelligibility. The clinical standard measurement procedure requires a professional experimenter to record and evaluate the response (expert-conducted speech audiometry). The use of automatic speech recognition enables self-conducted measurements with an easy-to-use speech-based interface. This article compares self-conducted SRT measurements using smart speakers with expert-conducted laboratory measurements. With smart speakers, there is no control over the absolute presentation level, potential errors from the automated response logging, and room acoustics. We investigate the differences between highly controlled measurements in the laboratory and smart speaker-based tests for young normal-hearing (NH) listeners as well as for elderly NH, mildly and moderately hearing-impaired listeners in low, medium, and highly reverberant room acoustics. For the smart speaker setup, we observe an overall bias in the SRT result that depends on the hearing loss. The bias ranges from +0.7 dB for elderly moderately hearing-impaired listeners to +2.2 dB for young NH listeners. The intrasubject standard deviation is close to the clinical standard deviation (0.57/0.69 dB for the young/elderly NH compared with 0.5 dB observed for clinical tests and 0.93/1.09 dB for the mild/moderate hearing-impaired listeners compared with 0.9 dB). For detecting a clinically elevated SRT, the speech-based test achieves an area under the curve value of 0.95 and therefore seems promising for complementing clinical measurements.
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Original Article
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720343/ https://doi.org/10.1177/2331216520970011 http://www.ncbi.nlm.nih.gov/pubmed/33272109
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Modeling Binaural Unmasking of Speech Using a Blind Binaural Processing Stage
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In: Trends Hear (2020)
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Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise
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Are Experienced Hearing Aid Users Faster at Grasping the Meaning of a Sentence Than Inexperienced Users? An Eye-Tracking Study
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Age-related differences in lexical access relate to speech recognition in noise
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Are experienced hearing aid ssers faster at grasping the meaning of a sentence than inexperienced ssers? An eye-tracking study
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International Collegium of Rehabilitative Audiology (ICRA) recommendations for the construction of multilingual speech tests ...
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How Hearing Impairment Affects Sentence Comprehension: Using Eye Fixations to Investigate the Duration of Speech Processing
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Influence of vocabulary knowledge & lexical access times on speech intelligibility in different acoustic conditions
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An Eye-Tracking Paradigm for Analyzing the Processing Time of Sentences with Different Linguistic Complexities
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International kompatible und multilingual einsetzbare Sprachtests im Störschall ...
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