Though the importance of spectral characteristics of Korean vowels in the hVd syllable has been recognized in the literature, it has never been studied whether static spectral measurements at a (steady-state) central section are enough to characterize Korean vowels in spontaneous speech, or dynamic spectral measurements across the temporal dimension can better characterize vowels. Despite ample reported evidence of the perceptual influence of non-spectral cues on spectral properties of vowels in the literature, no reports have yet been released on the difference in the degree of the perceptual influence of non-spectral cues (e.g., place and manner of the preceding or following phones, F0, speaking rate, prosody, and gender) on spectral properties of vowels. Through Neural Network pattern recognition modeling in a supervised mode, it was found that dynamic spectral models with non-spectral cues better explain vowel perception than static spectral models and furthermore, flanking phone identities, and manner and place of flanking phones are perceptually the most influential while duration, F0 and speaking rate are perceptually far less contributive than argued in the literature.

1. Introduction
 2. The Perceptual Influence of Non-Spectral Cues on SpectralProperties of Vowels
  2.1. The Influence of F0
  2.2. The Influence of Duration
  2.3. The Influence of Gender
  2.4. The Influence of Neighboring Phone Identity, Manner and Place
  2.5. The Influence of Speaking Rate, Prosody, and Others
 3. Method
  3.1. Subjects
  3.2. Acoustic Characteristics of Target Monophthongal Vowel Signals
  3.3. Parameters Extracted for the Classification of the Vowel Signals
 4. Results
  4.1. Spectral Model Classification Accuracy Comparison
 5. Discussion
  5.1. The Individual Perceptual Influence of Non-Spectral Cues on aOne-Sampled Spectral Model (F123_50)
  5.2. The Individual Perceptual Influence of Non-Spectral Cues onTwo-Sampled Spectral Models (F123_2080, F123_2050, andF123_5080)
  5.3. The Individual Perceptual Influence of Non-Spectral Cues on a Three-Sampled Spectral Model (F123_205080)
  5.4. Further Discussion on the Individual Perceptual Influence ofNon-Spectral Cues
  5.5. Classification Performance of the Spectral Models with All theParameters fed in
 6. Conclusion
 References

• Soonhyun Hong(Inha University)