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Deep Learning Approaches for Spoken and Natural Language Processing pp 41–53Cite as

Optimal Fractal Feature Selection and Estimation for Speech Recognition Under Mismatched Conditions

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Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Children are likely to have trouble pronouncing vowels in real-life conversations. Therefore, it becomes necessary to handle the correct pronunciation corresponding to every vowel for the improved efficiency of Automatic Speech Recognition (ASR) system. In this research, the linguistic study of native speakers and their auditory inconsistency was pursued using the extraction of efficient front-end speech vectors utilizing three varying fractal dimensions (FD) : Higuchi FD, Katz FD, and Petrosian FD. These depicted fractal measurements are based on precise evaluation of FD, which serves as a key parameter of fractal geometry, thus helping in much easier representation of complex shapes in an input signal as compared to conventional speech parameters. Furthermore, experimental results on the use of these short-term fractal components on pooling with Mel frequency cepstral coefficients (MFCC) have been recorded with modest changes using hidden Markov models (HMM). The selection of optimal features was made possible by increasing child data through adaptation measures on adult data, which has allowed for the examination of new features under mismatched conditions resulting in an overall improvement of 11.54% in the performance of the proposed ASR framework.

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Conflict of Interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Centre of Excellence for Speech and Multimodal Laboratory, Chitkara University Institute of Engineering & Technology, Chitkara University, Chandigarh, Punjab, India

    Puneet Bawa & Vaibhav Kumar

  2. Speech and Language Research Centre (SLRC), School of Computer Science, University of Petroleum & Energy Studies, Dehradun, Uttarakhand, India

    Virender Kadyan

  3. Chitkara University Institute of Engineering & Technology, Chitkara University, Chandigarh, Punjab, India

    Archana Mantri

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  1. Puneet Bawa
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  2. Virender Kadyan
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  3. Archana Mantri
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  4. Vaibhav Kumar
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Correspondence to Virender Kadyan .

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Editors and Affiliations

  1. University of Petroleum and Energy Studies, Dehradun, India

    Virender Kadyan

  2. Jagat Guru Nanak Dev Punjab State Open University, Patiala, Punjab, India

    Amitoj Singh

  3. Kyoto Sangyo University, Kyoto, Japan

    Mohit Mittal

  4. Amman Arab University, Amman, Jordan

    Laith Abualigah

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Bawa, P., Kadyan, V., Mantri, A., Kumar, V. (2021). Optimal Fractal Feature Selection and Estimation for Speech Recognition Under Mismatched Conditions. In: Kadyan, V., Singh, A., Mittal, M., Abualigah, L. (eds) Deep Learning Approaches for Spoken and Natural Language Processing. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-79778-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-79778-2_3

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