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Development of Output Correction Methodology for Long Short Term Memory-Based Speech Recognition

Author

Listed:
  • Recep Sinan Arslan

    (Department of Computer Programming, Vocational School of Technical Science, Yozgat Bozok University, 66200 Yozgat, Turkey)

  • Necaattin Barışçı

    (Department of Computer Engineering, Faculty of Technology, Gazi University, 06560 Ankara, Turkey)

Abstract

This paper presents a correction methodology for Long Short Term Memory (LSTM) based speech recognition. A strategy that validates with a reference database was developed for LSTM. It is conceptually simple but requires a large keyword database to match test templates. The correction method is based on the “most matching method” that is finding the word in which the system output is closest among the “Referenced Template Database”. Each LSTM model recognition output was corrected with the proposed new concept. Thus, system recognition performance was improved by correcting faulty outputs. The effectiveness, efficiency, and contribution of this approach to system performance were demonstrated by experiments. Tests carried out using different speech-text datasets and LSTM models yielded an average performance increase of 2.25%. With some advanced models, this ratio rises to 3.84%.

Suggested Citation

  • Recep Sinan Arslan & Necaattin Barışçı, 2019. "Development of Output Correction Methodology for Long Short Term Memory-Based Speech Recognition," Sustainability, MDPI, vol. 11(15), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4250-:d:255225
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    Cited by:

    1. Miltiadis D. Lytras & Anna Visvizi, 2021. "Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making," Sustainability, MDPI, vol. 13(7), pages 1-3, March.
    2. Sun, Ying & Zhang, Luying & Yao, Minghui, 2023. "Chaotic time series prediction of nonlinear systems based on various neural network models," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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