Dimensionality analysis of singing speech based on locality preserving projections

In this study, we expand the question of ”what is the intrinsic dimensionality of speech?” to ”how does the intrinsic dimensionality of speech change from speaking to singing?”. Our focus is on dimensionality of the vowel space regarding spectral features, which is important in acoustic modeling applications. Locality Preserving Projection (LPP) is applied for dimensionality reduction of the spectral feature vectors, and vowel classification performance is studied in low-dimensional subspaces. Performance analysis of singing and speaking vowel classification based on reducing the dimension shows that compared to speaking, a higher number of dimensions is required for effective representation of singing vowels. The results are also explained in terms of differences in the formant spaces of singing and speaking, and vowel classification performance is analyzed based on feature vectors consisting of formant frequencies. The formant analysis results are shown to be consistent with LPP dimensionality analysis, which verifies the inherent dimensionality increase of the vowel space from speaking to singing.