Threshold-Based Noise Detection and Reduction for Automatic Speech Recognition System in Human-Robot Interactions

Abstract

This work develops a speech recognition system that uses two procedures of proposed noise detection and combined noise reduction. The system can be used in applications that require interactive robots to recognize the contents of speech that includes ambient noise. The system comprises two stages, which are the threshold-based noise detection and the noise reduction procedure. In the first stage, the proposed system automatically determines when to enhance the quality of speech based on the signal-to-noise ratio (SNR) values of the collected speech at all times. In the second stage, independent component analysis (ICA) and subspace speech enhancement (SSE) are employed for noise reduction. Experimental results reveal that the SNR values of the enhanced speech exceed those of the received noisy speech by approximately 20 dB to 25 dB. The noise reduction procedure improves the speech recognition rates by around 15% to 25%. The experimental results indicate that the proposed system can reduce the effect of noise in numerous noisy environments and improve the quality of speech for recognition purposes.

Automatic Speech Recognition: Systematic Literature Review

“…In [34] the authors employed two stages that detect and redact the environment noise to perform speech recognition in human-robot interactions. The proposed system automatically determines how to enhance speech quality based on the signal-to-noise ratio (SNR).…”

“…Although many noise reduction methods have been developed, these methods cannot work unless the noises are known. However, noise signals can have many properties in real-world situations [34]. Due to this variety, creating a database that is responsive to external noise is a big challenge for DNN-based systems [39].…”

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

“…As technology progresses, speech recognition will be embedded in more devices used in everyday activities, where environmental variables perform a major part, such as mobile phone voice recognition applications [10], cars [11], integrated access control and information systems [12], emotion identification systems [13], application monitoring [14], disabled assistance [15], and intelligent technology. In addition to voice, many acoustic applications are also essential in diverse engineering issues [16][17][18][19][20][21][22]. A noise decrease method could be deployed to enhance efficiency in real-world noisy settings [23][24][25][26].…”

Hybrid Filter for Enhancing Input Microphone-Based Discriminative Model

“…However, the approach was slow as compared with the proposed one when applied on speech recognition, since it is based on wavelet. Lee et al [5] proposed a procedure based on a system that recognizes the content of speech that has ambient noise. The work consisted of two stages, the first is the automatic enhancement of the quality of speech, depending on the signal-to-noise ratio (SNR), and the second is noise reduction by using the subspace speech enhancement.…”