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Improved structure-based automatic estimation of pronunciation proficiency

Automatic estimation of pronunciation proficiency has its specific difficulty. Adequacy in controlling the vocal organs is often estimated from spectral envelopes of input utterances but the envelope patterns are also affected by alternating speakers. To develop a good and stable method for automatic estimation, the envelope changes caused by linguistic factors and those by extra-linguistic factors should be properly separated. In our previous study [1], to this end, we proposed a mathematicallyguaranteed and linguistically-valid speaker-invariant representation of pronunciation, called speech structure. After the proposal, we have tested that representation also for ASR [2, 3, 4] and, through these works, we have learned better how to apply speech structures for various tasks. In this paper, we focus on a proficiency estimation experiment done in [1] and, using the recently developed techniques for the structures, we carry out that experiment again but under different conditions. Here, we use a smaller unit of structural analysis, speaker-invariant substructures, and relative structural distances between a learner and a teacher. Results show higher correlation between human and machine rating and also show extremely higher robustness to speaker differences compared to widely used GOP scores.