Improvement of Speech Detection Using ERB Feature Extraction

Abstract

A range of speech extraction techniques have been applied to improve speech recognition when the signals are mixed with noise. Degradation of the speech recognition performance is caused by differences between the model training environment and the recognition environment due to inaccurate voice versus non-voice classification at low signal-to-noise ratios (SNRs). Problems also arise because voice activity detection is inaccurate when noise is caused by inconsistent changes in the recognition environment and the learning model. One technique is to extract a speech feature that is resistant to noise by removing that noise to improve the speech recognition performance. This study extracted such a feature using an equivalent rectangular bandwidth (ERB) filter bank cepstrum and constructed a learning model using the acoustic model to improve the speech recognition rate. The ERB filter bank cepstrum was examined in a computational auditory scene analysis system, which analyzes the properties of the speech signal. This paper improved the speech recognition rate by extracting such a feature with an ERB filter bank cepstrum. The proposed model used train and train station noises to evaluate the performance. The distortion was measured by performing noise reduction at SNRs of $$-10$$ - 10 and $$-5$$ - 5 dB in noisy environments, showing a respective 1.67 and 1.74 dB improvement in performance.