Title :
Energy-constrained signal subspace method for speech enhancement and recognition
Author :
Huang, Jun ; Zhao, Yunxin
Author_Institution :
Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA
Abstract :
In this letter, an improved signal-subspace-based speech enhancement algorithm is proposed for automatic speech recognition under an additive noise environment. The key idea is to match the short-time energy of the enhanced speech signal to the unbiased estimate of the short-time energy of the clean speech, which is proven very effective for improving the estimation of the low-energy segments of continuous speech under low signal-to-noise ratio (SNR) conditions. Experimental results show significant improvement in both the segmental SNR and the word recognition accuracy of the enhanced speech under SNR conditions of 10-20 dB.
Keywords :
acoustic noise; estimation theory; speech enhancement; speech recognition; transforms; 10 to 20 dB; additive noise environment; automatic speech recognition; clean speech; energy-constrained signal subspace method; enhanced speech; low signal-to-noise ratio conditions; low-energy segments; segmental SNR; short-time energy; speech enhancement; unbiased estimate; word recognition accuracy; Additive noise; Automatic speech recognition; Interference; Karhunen-Loeve transforms; Signal processing; Signal to noise ratio; Speech analysis; Speech enhancement; Speech recognition; White noise;
Journal_Title :
Signal Processing Letters, IEEE
