Data-driven Detection and Analysis of the Patterns of Creaky Voice
Table 2: Mutual information-based measures for the best creaky features. On the diagonal - orange: the relative intrinsic information I(Xi;C)H(C). In the bottom-left part - light orange: the relative redundancy between the two considered features I(Xi;Xj;C)H(C). In the top-right part - white: the relative joint information of the two considered features I(Xi,Xj;C)H(C).
['[EMPTY]', '[EMPTY]', 'KD H2-H1', 'KD Peak-Prom', 'KD F0creak', 'ISHI’S IFP', 'ISHI’S IPS', 'ISHI’S PwP_fall', 'ISHI’S PwP_rise']
The best combination of two features is H2-H1 with Peak-Prom (0.48). Nonetheless it is of interest to observe that the association of H2-H1 with Ishi’s features (except IFP) carries out comparable results (between 0.46 and 0.47), albeit that these latter features, individually, display less intrinsic information. This is possible thanks to the weak redundancy of Ishi’s features with H2-H1 (varying only between 0.03 and 0.06). This good complementarity can then be turned into an advantage for building up an automatic detection system. One should note that Ishi’s features, as opposed to the KD features, were not developed to be individual detectors of creaky voice, but rather their combination is what is relevant in this regard (i.e. detecting candidate creaky regions, and eliminating normal voiced and unvoiced candidate regions). This is evident here by the negative value (-0.06) for relative redundancy between IFP and IPS, indicating a degree of synergy between the two parameters. However, aside from this pair features inside a category (i.e. KD or Ishi’s features) are quite redundant with each other while features from two different categories exhibit an interesting complementarity.