Improvement of Speech Detection Using ERB Feature Extraction

Oh, Sang-Yeob; Chung, Kyungyong

doi:10.1007/s11277-014-1752-9

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…Speech recognition performance in noisy car environments is demonstrated to be improved by combining microphone array processing techniques with a visual-audio Voice Activity Detector (VAD) [5]. The proposed localization framework combined with delay and beamforming yields a 7.1 A learning model using acoustic models to increase speech recognition rates is created [6]. In speech processing applications, noise processing for speech recognition systems is often expressed as a digital filtering process where noisy speech is passed through a linear filter to obtain clean speech predictions.…”

Section: Related Workmentioning

confidence: 99%

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

ÖZKURT

2024

Preprint

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This study investigates the effects of audio processing and filtering strategies to enhance the performance of speech recognition systems in noisy environments. The focus is on the Short-Time Fourier Transform (STFT) operations applied to noisy audio files and noise reduction procedures. While STFT operations form the basis for detecting noise and analyzing the speech signal in the frequency domain, noise reduction steps involve threshold-based masking and convolution operations. The results obtained demonstrate a significant improvement in speech recognition accuracy in noisy environments through audio processing and filtering strategies. A detailed analysis of the graphs provides guidance for evaluating the effectiveness of noise reduction procedures and serves as a roadmap for future research. This study emphasizes the critical importance of audio processing and filtering strategies in improving the performance of speech recognition systems in noisy environments, laying a foundation for future studies.

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Section: Related Workmentioning

confidence: 99%

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

ÖZKURT

2024

Preprint

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“…The feature extraction procedure extracts some of the popular features like mel-frequency spectra and perceptual linear prediction (PLP) coefficients. The features can also be extracted using a rectangular equivalent bandwidth (ERB) to increase the rate of speech recognition (Oh & Chung, 2014). Amplitude modulation spectrogram (AMS) design has also been used for speech recognition for increasing its performance by noise attenuation (Ma & Zhou, 2008).…”

Section: Introductionmentioning

confidence: 99%

A novel stochastic deep resilient network for effective speech recognition

Shukla

Jain

2021

Int J Speech Technol

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Speech recognition is a subjective occurrence. This work proposes a novel stochastic deep resilient network(SDRN) for speech recognition. It uses a deep neural network (DNN) for classification to predict the input speech signal. The hidden layers of DNN and its neurons are additionally optimized to reduce the computation time by using a neural-based opposition whale optimization algorithm (NOWOA). The novelty of the SDRN network is in using NOWOA to recognize large vocabulary isolated and continuous speech signals. The trained DNN features are then utilized for predicting isolated and continuous speech signals. The standard database is used for training and testing. The real-time data (recorded in ambient condition) for isolated words and continuous speech signals are additionally used for validation to increase the accuracy of the SDRN network. The proposed methodology unveils an accuracy of 99.6% and 98.1% for isolated words (standard and real-time) database and 98.7% for continuous speech signal (real-time). The obtained results exhibit the supremacy of SDRN over other techniques.

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“…[22] proposed a novel feature extraction algorithm based on the double-combined Fourier transform and envelope line fitting is proposed. [23] extracted feature using an equivalent rectangular bandwidth (ERB) filter band cepstrum and constructed a learning model using the acoustic model to improve the speech detection and recognition, etc. Those methods take the advantages of various characteristic and intelligence algorithm, improve the detection effect under noise environment effectively, but their algorithmically complex increased sharply, and the detection effect remains unsatisfactory under low SNR, especially for non-stationary noise.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Voice Activity Detection Algorithm

Zhang

Huang

2015

International Journal on Smart Sensing and Intelligent Systems

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Voice Activity Detection (VAD) is a crucial step for speech processing, which detecting accuracy and speed directly affects the effect of subsequent processing. Some voice processing system based phone or in the indoor environment, which need simple and quick method of VAD, for these representative voice signal, this paper proposes a new algorithm which is adaptive and quick based on a major improvement to Dual-Threshold endpoint detection algorithm. First the amplitude normalization is processed to the original voice signal, the characteristic is extracted by means of short-time amplitude, which can simplify operation. Then, large-scale (long frame-length and frame-shift) short-time amplitude is used for rough detection, combining adaptive threshold judgement of consecutive frames, which can find voice areas of start-point and end-point quickly. To these areas, small-scale (short frame-length and frame-shift) short-time amplitude is used for accurate detection, forward scanning is put to start-point area, reverse scanning is put to end-point area, combining adaptive threshold judgement of consecutive frames, start-point and end-point of the effective speech can be accurately located. Experimental results show that the method of this paper can detect endpoints of voice signal more quickly and accurately, which can improve recognition performance dramatically. Largescale can increase detection speed, small-scale can improve detection accuracy, both can be adjusted to satisfy the different requirements. The method of this paper ensures both detection speed and precision, which has more flexibility and applicability.

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Improvement of Speech Detection Using ERB Feature Extraction

Cited by 14 publications

References 22 publications

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

Investigation of the Effectiveness of Audio Processing and Filtering Strategies in Noisy Environments on Speech Recognition Performance

A novel stochastic deep resilient network for effective speech recognition

An Adaptive Voice Activity Detection Algorithm

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