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Enhancing the Feature Extraction Process for Automatic Speech Recognition with Fractal Dimensions

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Abstract

Mel frequency cepstral coefficients (MFCCs) are a standard tool for automatic speech recognition (ASR), but they fail to capture part of the dynamics of speech. The nonlinear nature of speech suggests that extra information provided by some nonlinear features could be especially useful when training data are scarce or when the ASR task is very complex. In this paper, the Fractal Dimension of the observed time series is combined with the traditional MFCCs in the feature vector in order to enhance the performance of two different ASR systems. The first is a simple system of digit recognition in Chinese, with very few training examples, and the second is a large vocabulary ASR system for Broadcast News in Spanish.

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Acknowledgments

The authors thank Roger Jang and Infozazpi irratia for providing the basic resources for this work.

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Authors and Affiliations

  1. Department of Systems Engineering and Automation, University of the Basque Country UPV/EHU, Europa plaza 1, 20018, Donostia, Spain

    Aitzol Ezeiza, Karmele López de Ipiña & Nora Barroso

  2. Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Manuel Lardizabal 1, 20018, Donostia, Spain

    Carmen Hernández

Authors
  1. Aitzol Ezeiza
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  2. Karmele López de Ipiña
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  3. Carmen Hernández
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  4. Nora Barroso
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Correspondence to Aitzol Ezeiza.

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Ezeiza, A., López de Ipiña, K., Hernández, C. et al. Enhancing the Feature Extraction Process for Automatic Speech Recognition with Fractal Dimensions. Cogn Comput 5, 545–550 (2013). https://doi.org/10.1007/s12559-012-9165-0

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