The importance of formants and spectral shape was investigated for vowel perception in severe noise. Twelve vowels were synthesized using two different synthesis methods, one where the original spectral detail was preserved, and one where the vowel was represented by the spectral peaks of the first three formants. In addition, formants F1 and F2 were suppressed individually to investigate the importance of each in severe noise. Vowels were presented to listeners in quiet and in speechshaped noise at signal to noise ratios (SNRs) of 0, À5, and À10 dB, and vowel confusions were determined in a number of conditions. Results suggest that the auditory system relies on formant information for vowel perception irrespective of the SNR, but that, as noise increases, it relies increasingly on more complete spectral information to perform formant extraction. A second finding was that, while F2 is more important in quiet or low noise conditions, F1 and F2 are of similar importance in severe noise.

No abstract

Feature information transmission analysis (FITA) estimates information transmitted by an acoustic feature by assigning tokens to categories according to the feature under investigation and comparing within-category to between-category confusions. FITA was initially developed for categorical features (e.g., voicing) for which the category assignments arise from the feature definition. When used with continuous features (e.g., formants), it may happen that pairs of tokens in different categories are more similar than pairs of tokens in the same category. The estimated transmitted information may be sensitive to category boundary location and the selected number of categories. This paper proposes a fuzzy approach to FITA that provides a smoother transition between categories and compares its sensitivity to grouping parameters with that of the traditional approach. The fuzzy FITA was found to be sufficiently robust to boundary location to allow automation of category boundary selection. Traditional and fuzzy FITA were found to be sensitive to the number of categories. This is inherent to the mechanism of isolating a feature by dividing tokens into categories, so that transmitted information values calculated using different numbers of categories should not be compared. Four categories are recommended for continuous features when twelve tokens are used.