Enhancing Speech Signal Features with Linear Envelope Subtraction

Duc, Hao; Chau, Duc Thanh; Nguyễn, Duc Tung; Son, Tran Hung

doi:10.1007/978-3-030-88113-9_25

Cited by 5 publications

(1 citation statement)

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“…With the high frequency, the increase of n leads to the value of ⟨ax + v, sin(nx)⟩ reduced significantly. In [33], Do et al showed that with the threshold for computing error under 10 −8 , the impacted range of spectrum ends at 6300Hz.…”

Section: G Distortion In Spectral Representation Caused By Linear Env...mentioning

confidence: 99%

Exploiting Signal Linear Trend in the Time Domain to Enhance Speech Feature

Duc

2022

IEEE Access

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Speech feature extraction usually begins with transforming the signal from the time domain to the frequency domain via integral transforms. Human speech contains sine-shape waves and a linear trend in the time domain, usually ignored. This research proposes two new methods to strengthen the speech feature vector using this unused factor. Before transforming the speech frames into the frequency domain, we use linear regression to identify each speech frame's linear trend or linear envelope in the time domain. Then we remove the impact of that trend to normalize the signal and emphasize the stationary elements in the signal. The proposed feature vector includes parameters from the linear envelope and the conventional vectors as a spectrum or MFCC. Experimental results demonstrate that the impact of the linear envelope is significant and the linear envelope subtraction is a meaningful stage. Our new features emphasize the stationary property in the speech signal and improve the result for speech recognition in terms of error rate.

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Section: G Distortion In Spectral Representation Caused By Linear Env...mentioning

confidence: 99%