AI Article Synopsis
- This study focuses on improving automatic speaker gender identification by incorporating non-linear parameters, specifically fractal dimension, along with traditional linear parameters like pitch.
- The research utilizes the lifting scheme to analyze pitch and applies Takens theory for reconstructing the phase space of the fractal dimension, enhancing the feature space used for identification.
- Experimental results indicate that the combination of multiple dimensional features (pitch, fractal dimension, and fractal dimension complexity) yields more accurate and robust gender identification compared to using single-dimensional linear parameters.
Article Abstract
Automatic speaker gender identification based on voice feature is an important task in voice processing and analysis fields. In this paper non-linear parameters such as fractal dimension are applied to be one part of feature space for improving the ability of describing speaker gender feature through conventional linear parameters method. Pitch is picked using lifting scheme, and audio fractal dimension is extracted. Then based on Takens theory, the time delay method is used to reconstruct the phase space of fractal dimension sequence. And fractal dimension complexity is obtained by calculating Approximate Entropy. Three dimension feature vectors, including the pitch, the fractal dimension and the fractal dimension complexity, are applied to speaker gender identification. Experiment results show that through adding non-linear parameters, compared with the linear parameter using one dimension only such as pitch, the proposed method is more accurate and robust, and thus provides a new way for speaker gender identification.
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