We present a complete system for image-based 3D vocal tract analysis ranging from MR image acquisition during phonation, semi-automatic image processing, quantitative modeling including model-based speech synthesis, to quantitative model evaluation by comparison between recorded and synthesized phoneme sounds. For this purpose, six professionally trained speakers, age 22-34y, were examined using a standardized MRI protocol (1.5 T, T1w FLASH, ST 4mm, 23 slices, acq. time 21s). The volunteers performed a prolonged (> or = 21s) emission of sounds of the German phonemic inventory. Simultaneous audio tape recording was obtained to control correct utterance. Scans were made in axial, coronal, and sagittal planes each. Computer-aided quantitative 3D evaluation included (i) automated registration of the phoneme-specific data acquired in different slice orientations, (ii) semi-automated segmentation of oropharyngeal structures, (iii) computation of a curvilinear vocal tract midline in 3D by nonlinear PCA, (iv) computation of cross-sectional areas of the vocal tract perpendicular to this midline. For the vowels /a/,/e/,/i/,/o/,/ø/,/u/,/y/, the extracted area functions were used to synthesize phoneme sounds based on an articulatory-acoustic model. For quantitative analysis, recorded and synthesized phonemes were compared, where area functions extracted from 2D midsagittal slices were used as a reference. All vowels could be identified correctly based on the synthesized phoneme sounds. The comparison between synthesized and recorded vowel phonemes revealed that the quality of phoneme sound synthesis was improved for phonemes /a/, /o/, and /y/, if 3D instead of 2D data were used, as measured by the average relative frequency shift between recorded and synthesized vowel formants (p < 0.05, one-sided Wilcoxon rank sum test). In summary, the combination of fast MRI followed by subsequent 3D segmentation and analysis is a novel approach to examine human phonation in vivo. It unveils functional anatomical findings that may be essential for realistic modelling of the human vocal tract during speech production.
[1]
J. Mazziotta,et al.
Rapid Automated Algorithm for Aligning and Reslicing PET Images
,
1992,
Journal of computer assisted tomography.
[2]
Gunnar Fant,et al.
Acoustic Theory Of Speech Production
,
1960
.
[3]
P. W. Nye,et al.
Analysis of vocal tract shape and dimensions using magnetic resonance imaging: vowels.
,
1991,
The Journal of the Acoustical Society of America.
[4]
Teuvo Kohonen,et al.
Self-Organizing Maps
,
2010
.
[5]
E. Hoffman,et al.
Vocal tract area functions from magnetic resonance imaging.
,
1996,
The Journal of the Acoustical Society of America.
[6]
Shrikanth S. Narayanan,et al.
Toward articulatory-acoustic models for liquid approximants based on MRI and EPG data. Part I. The laterals
,
1997
.
[7]
Ralf Der,et al.
Second-Order Learning in Self-Organizing Maps
,
1999
.
[8]
Erkki Oja,et al.
Kohonen Maps
,
1999,
Encyclopedia of Machine Learning.
[9]
Didier Demolin,et al.
Segmentation of the airway from the surrounding tissues on magnetic resonance images: a comparative study
,
1998,
ICSLP.
[10]
P. Mermelstein.
Articulatory model for the study of speech production.
,
1973,
The Journal of the Acoustical Society of America.
[11]
Man Mohan Sondhi,et al.
A hybrid time-frequency domain articulatory speech synthesizer
,
1987,
IEEE Trans. Acoust. Speech Signal Process..