@article {10.3844/jcssp.2020.1731.1741,
article_type = {journal},
title = {Automatic Re-Formulation of user’s Irrational Behavior in Speech Recognition using Acoustic Nudging Model},
author = {Ajayi, Lydia Kehinde and Azeta, Ambrose and Odun-Ayo, Isaac and Chidozie, Felix and Taiwo, Ajayi Peter},
volume = {16},
number = {12},
year = {2020},
month = {Dec},
pages = {1731-1741},
doi = {10.3844/jcssp.2020.1731.1741},
url = {https://thescipub.com/abstract/jcssp.2020.1731.1741},
abstract = {In automatic speech recognition for development of automatic speech recognition applications, there has been numerous claims on the presence of speech recognition errors known as classified into lexical and acoustic errors. These errors distort speech signals thereby depreciating the accuracy and performance rate of speech recognition applications. Even though lexical speech recognition error problem has been partially combated, acoustic speech recognition error referred to as user’s acoustic irrational behavior is being ignored causing high error rate with low accuracy which is the bone of contention and an impediment factor in the wide adoption of speech recognition technology. Users do not always behave in a rational manner especially when dealing with a particular speech recognition application. The persistent presence of these user’s acoustic irrational behavior in speech have intensified the essential need to automatically detect and correct such errors, as current researches only focus on detecting user’s acoustic irrational behavior but not correcting/reformulating/re-sizing this error. Hence, this paper provides an acoustic nudging model that will perform automatic correction/reformulation of user’s acoustic irrational behavior in speech to achieve higher performance and accuracy using different acoustic parameters which are based in Pitch, Time gaps between words, Timbre descend and ascend time and Loudness. This study was able to discover a foundation for reducing error rate and achieve higher performance, as well as improve accuracy in speech recognition applications through detection and re-formulation of user’s acoustic irrational behavior in speech signal automatically, thereby making the model applicable to any speech recognition applications. The outcome of this study would be useful in enhancing accuracy and performance in the context of automatic speech recognition.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}