Intrinsic Fourier analysis on the manifold of speech sounds

Recently, there has been much interest in geometrically motivated dimensionality reduction algorithms. These algorithms exploit low-dimensional manifold structure in certain natural datasets to reduce dimensionality while preserving categorical content. This paper has two goals: (i) to motivate the existence of a low-dimensional curved manifold structure to voiced speech sounds, and (ii) to present a new intrinsic (manifold-based) spectrogram technique founded on the existence this manifold structure. We find that the intrinsic representation allows phonetic distinction in fewer dimensions than required by a traditional spectrogram.