No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Phoneme categorization relying solely on high-frequency energy
1.ANSI (1992). ANSI S3.42-1992, American National Standard Testing Hearing Aids with a Broad-Band Noise Signal ( American National Standards Institute, New York).
2. Apoux, F. , and Bacon, S. P. (2004). “ Relative importance of temporal information in various frequency regions for consonant identification in quiet and in noise,” J. Acoust. Soc. Am. 116, 1671–1680.
3. Fullgrabe, C. , Baer, T. , Stone, M. A. , and Moore, B. C. J. (2010). “ Preliminary evaluation of a method for fitting hearing aids with extended bandwidth,” Int. J. Audiol. 49, 741–753.
5. LeGendre, S. J. , Liss, J. M. , and Lotto, A. J. (2009). “ Discriminating dysarthria type and predicting intelligibility from amplitude modulation spectra,” J. Acoust. Soc. Am. 125, 2530.
7. Miller, G. A. , and Nicely, P. E. (1955). “ An analysis of perceptual confusions among some English consonants,” J. Acoust. Soc. Am. 27, 338–352.
8. Monson, B. B. , Hunter, E. J. , Lotto, A. J. , and Story, B. H. (2014a). “ The perceptual significance of high-frequency energy in the human voice,” Front. Psych. 5, 587.
9. Monson, B. B. , Hunter, E. J. , and Story, B. H. (2012a). “ Horizontal directivity of low- and high-frequency energy in speech and singing,” J. Acoust. Soc. Am. 132, 433–441.
10. Monson, B. B. , Lotto, A. J. , and Story, B. H. (2012b). “ Analysis of high-frequency energy in long-term average spectra of singing, speech, and voiceless fricatives,” J. Acoust. Soc. Am. 132, 1754–1764.
11. Monson, B. B. , Lotto, A. J. , and Story, B. H. (2014b). “ Detection of high-frequency energy level changes in speech and singing,” J. Acoust. Soc. Am. 135, 400–406.
12. Moore, B. C. J. (2012). “ Effects of bandwidth, compression speed, and gain at high frequencies on preferences for amplified music,” Trends Amplif. 16, 159–172.
13. Moore, B. C. J. , Fullgrabe, C. , and Stone, M. A. (2010). “ Effect of spatial separation, extended bandwidth, and compression speed on intelligibility in a competing-speech task,” J. Acoust. Soc. Am. 128, 360–371.
14. Moore, B. C. J. , Stone, M. A. , Fullgrabe, C. , Glasberg, B. R. , and Puria, S. (2008). “ Spectro-temporal characteristics of speech at high frequencies, and the potential for restoration of audibility to people with mild-to-moderate hearing loss,” Ear Hear. 29, 907–922.
15. Pollack, I. (1948). “ Effects of high pass and low pass filtering on the intelligibility of speech in noise,” J. Acoust. Soc. Am. 20, 259–266.
16. Pulakka, H. , Laaksonen, L. , Yrttiaho, S. , Myllyla, V. , and Alku, P. (2012). “ Conversational quality evaluation of artificial bandwidth extension of telephone speech,” J. Acoust. Soc. Am. 132, 848–861.
17. Shannon, R. V. , Zeng, F. G. , Kamath, V. , Wygonski, J. , and Ekelid, M. (1995). “ Speech recognition with primarily temporal cues,” Science 270, 303–304.
Article metrics loading...
Speech perception studies generally focus on the acoustic information present in the frequency regions below 6 kHz. Recent evidence suggests that there is perceptually relevant information in the higher frequencies, including information affecting speech intelligibility. This experiment examined whether listeners are able to accurately identify a subset of vowels and consonants in CV-context when only high-frequency (above 5 kHz) acoustic information is available (through high-pass filtering and masking of lower frequency energy). The findings reveal that listeners are capable of extracting information from these higher frequency regions to accurately identify certain consonants and vowels.
Full text loading...
Most read this month