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

Recep Sinan Arslan and Necaattin Barışçı
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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

Sustainability, 2019, vol. 11, issue 15, 1-16

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%.

Keywords: speech recognition; Long Short Term Memory (LSTM); speech output correction; most-matching (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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