An investigation of manifold learning for speech analysis

Due to the physiological constraints of articulatory motion the speech apparatus has limited degrees of freedom. As a result, the range of speech sounds a human is capable of producing may lie on a low dimensional submanifold of the high dimensional space of all possible sounds. In this study a number of manifold learning algorithms are applied to speech data in an effort to extract useful low dimensional structure from the high dimensional speech signal. The ability of these manifold learning algorithms to separate vowels in a low dimensional space is evaluated and compared to a classical linear dimensionality reduction method. Results indicate that manifold learning algorithms outperform classical methods in low dimensions and are capable of discovering useful manifold structure in speech data.