Bivariate analysis of disordered connected speech using temporal and spectral acoustic cues

The presentation concerns the assessment of disordered voices produced by dysphonic speakers. The empirical mode decomposition algorithm is used to decompose the log of the magnitude spectrum of the speech signal into its harmonic, envelope and noise components and the harmonic-to-noise ratio (HNR) is used to summarize the overall quality of the disordered voices. The present study aims at improving a previously proposed algorithm by incorporating an appropriate method that estimates automatically the thresholds required by the algorithm without knowledge of the fundamental frequency and combining the temporal acoustic marker named segmental signal-to-dysperiodicity ratio (SDRSEG) with the harmonic-to-noise ratio in order to predict the degree of perceived hoarseness. The performances of the bivariate analysis-based approach for vocal dysperiodicities assessment in terms of correlation of the predicted perceived grade scores with the original perceived degree of hoarseness are investigated using a large corpus comprising concatenations of two Dutch sentences followed by vowel [a].