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Robust Perceptual Wavelet Packet Features for the Recognition of Spontaneous Kannada Sentences

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Abstract

The speech community achieved significant improvements in the performance of ASR systems for English and other languages. But, the ASR for low-resource languages is still far from reality. The major cause for this problem is data scarcity, as ASR systems based on deep learning require huge data for training the system. An ASR for detecting and recognizing the spontaneously spoken low-resource Kannada language sentences is developed here. The proposed system is implemented over DNN-HMM hybrid acoustic models and n-gram language models. The efficacy of the proposed system is presented through the metricWord Error Rate (WER). The feature extraction is achieved through Perceptual Wavelets and Mel filter banks. The results reveal the competitive performance of the proposed ASR system over the baseline features and models.

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Data Availability Material

The datasets generated during and/or analysed during the current study are not publicly available due to the institute regulations, but are available from the corresponding author on reasonable request.

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Acknowledgements

Authors would like to express their sincere gratitude to the Management of Vidyavardhaka Sangha, Principal of VVCE, and HOD, Department of ECE, for their constant support and encouragement throught the completion of this work.

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The authors declare that no funds, grants were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Vidyavardhaka College of Engineering, Visvesvaraya Technological University, Mysuru, Karnataka, 570002, India

    Mahadevaswamy Shanthamallappa & D. J. Ravi

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  1. Mahadevaswamy Shanthamallappa
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  2. D. J. Ravi
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Contributions

All authors contributed to the study conception and design. Content preparation, data collection and analysis were performed by Mahadevaswamy. The first draft of the manuscript was written by Mahadevaswamy, and Editing, Writing and Final review was performed by DJR. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mahadevaswamy Shanthamallappa.

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The authors declare that they have no competing interests related to this research.

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The research conducted in this study adhered to the highest ethical standards, and is approved by the institutional review board. The study protocol and any subsequent modifications were reviewed and found to be in compliance with established ethical guidelines.

Informed Consent

Informed consent was obtained from all study participants or their legal representatives, as appropriate. Participants were provided with comprehensive information about the study's purpose, procedures, potential risks, and benefits, and their right to withdraw from the study at any time without consequences. Written consent was obtained from all participants before their inclusion in the research.

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To ensure participant anonymity and confidentiality, all personal identifiers were removed or anonymized from the data and any published materials. Participant identities were protected throughout the study, and data were stored securely in accordance with applicable data protection regulations.

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Shanthamallappa, M., Ravi, D.J. Robust Perceptual Wavelet Packet Features for the Recognition of Spontaneous Kannada Sentences. Wireless Pers Commun 133, 1011–1030 (2023). https://doi.org/10.1007/s11277-023-10802-9

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