Speech analysis-synthesis system using principal components of vowel spectra
Tomio Takara, Akichika Higa, Syouki Kaneshiro, Yuuya Oshiro · The Journal of the Acoustical Society of America · 2016
We have studied an effective method using principal components spanning a feature space of isolated vowels. A covariance matrix is calculated from many log-amplitude spectra of isolated vowels uttered by a speaker. An eigen equation of the covariance matrix is solved. The resulting eigenvectors are called principal vectors. In the analysis system, log-amplitude spectrum for each frame of a word uttered by the same speaker is transformed to the components on the principal vectors. In the synthesis system, a log-amplitude spectrum is reconstructed using the components on the principal vectors with the largest eigenvalues and the spoken word is synthesized using the LMA filter. We draw the distribution chart of the first and the second principal components extracted from Japanese vowel data. This figure was very similar to the F1—F2 distribution of vowels and so to the vowel classification map in coordinate axes of the degree of constriction and the tongue hump position. The Listening tests showed that the quality of the word with only 7 parameters per frame synthesized by the proposed method was comparable to the word with 9 to 12 parameters by the cepstral method. Consequently, the effectiveness of the proposed method was shown.