Speech Enhancement Modeling Towards Robust Speech Recognition System

TL;DR

This paper introduces a speech enhancement system designed to improve automatic speech recognition accuracy in noisy environments by reducing the impact of additive noise on speech signals.

Contribution

The paper presents a novel speech enhancement approach that significantly improves recognition accuracy under various noisy conditions compared to traditional methods.

Findings

Enhanced speech signals lead to higher recognition accuracy.

Performance improvement varies with noise type.

Speech enhancement effectively mitigates noise effects.

Abstract

Form about four decades human beings have been dreaming of an intelligent machine which can master the natural speech. In its simplest form, this machine should consist of two subsystems, namely automatic speech recognition (ASR) and speech understanding (SU). The goal of ASR is to transcribe natural speech while SU is to understand the meaning of the transcription. Recognizing and understanding a spoken sentence is obviously a knowledge-intensive process, which must take into account all variable information about the speech communication process, from acoustics to semantics and pragmatics. While developing an Automatic Speech Recognition System, it is observed that some adverse conditions degrade the performance of the Speech Recognition System. In this contribution, speech enhancement system is introduced for enhancing speech signals corrupted by additive noise and improving the performance of Automatic Speech Recognizers in noisy conditions. Automatic speech recognition experiments show that replacing noisy speech signals by the corresponding enhanced speech signals leads to an improvement in the recognition accuracies. The amount of improvement varies with the type of the corrupting noise.