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Published on: November 25, 2017
Effects of consonant environment on vowel formant patterns
J M Hillenbrand1, M J Clark, T M Nearey
1Speech Pathology and Audiology, Western Michigan University, Kalamazoo 49008, USA. james.hillenbrand@wmich.edu
The Journal of the Acoustical Society of America
|March 16, 2001
Summary
Vowel identification relies on spectral change patterns, even with varying consonant environments. Incorporating spectral change information significantly improves vowel category separation in speech perception models.
Area of Science:
- Speech perception research
- Acoustic phonetics
- Auditory processing
Background:
- Vowel identification is known to be influenced by spectral change patterns.
- Previous studies showed improved vowel category separability using formant pattern classifiers with multiple samples.
- Earlier research used a constant consonant-vowel-consonant (/hVd/) phonetic environment.
Purpose of the Study:
- To investigate if the relationship between vowel identity and spectral change patterns persists in varied consonant environments.
- To determine the impact of changing phonetic contexts on vowel perception.
- To assess the robustness of spectral change cues for vowel identification.
Main Methods:
- Recordings of eight vowels produced in isolation and CVC syllables by 12 speakers.
- CVC syllables included all combinations of seven initial and six final consonants.
- Formant frequencies (F1-F3) were measured every 5 ms using an interactive editing tool.
Main Results:
- Phonetic environment significantly affected formant patterns, especially for rounded vowels in alveolar contexts.
- Despite context-dependent shifts, spectral change information substantially improved vowel category separability.
- A pattern classifier using F0, duration, and two formant samples accounted for many listener behaviors.
Conclusions:
- Spectral change patterns remain crucial for vowel identification, even with varying consonant contexts.
- The study confirms the importance of dynamic spectral information over static formant patterns.
- Simple pattern classifiers incorporating spectral change effectively model aspects of human speech perception.
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