Abstract
Replay speech answer-sheet detection is an urgent problem to be solved for intelligent language learning system. Traditional features used in replay speech detection are often extracted from power spectrum. However, power spectrum may not be the optimal spectrum to extract feature for replay speech answer-sheet detection because it doesn't consider the characteristic of replay speech. In order to solve this limitation, this paper proposes a method of power spectrum decomposition for replay speech answer-sheet detection on intelligent language learning system. Log frame-wise normalization spectrum (LFNS) and log spectral energy (LSE) which consider the characteristic of replay speech, are obtained by decomposing log power spectrum based on constant-Q transform. Next, the other two features are obtained at the base of LFNS and LSE. The first is constant-Q normalization octave coefficients (CNOC) which is obtained by combining LFNS and octave subband transform. The second is CNOC-LSE that is obtained by combining CNOC and LSE. Then LFNS, CNOC and CNOC-LSE are fed into frame- and utterance-based neural networks. Experimental results show that the proposed LFNS can outperform the conventional log power spectrum, CNOC and CNOC-LSE can perform better than most of commonly used features. We found that utterance-based neural network outperforms frame-based neural network with the same inputs. In addition, handcrafted features give worse performance than corresponding spectrum for the utterance-based neural network while the opposite conclusion can be obtained for the frame-based neural network.
Highlights
Due to the development of deep learning, artificial intelligence (AI) technology has been applied in language teaching and learning recent years
In order to improve the performance of replay speech answer-sheet detection, this paper proposes a method to decompose power spectrum in log scale into log frame-wise normalization spectrum (LFNS) and log spectral energy (LSE), both of them consider the characteristic of replay speech
In order to detect replay speech answer-sheet, on the basis of considering the characteristic of replay speech answer, we propose a method to improve the performance of replay speech answer-sheet detection by decomposing log power spectrum into Log frame-wise normalization spectrum (LFNS) and LSE in this paper
Summary
Due to the development of deep learning, artificial intelligence (AI) technology has been applied in language teaching and learning recent years. That’s to say, if we can extract features from frame-wise normalization spectrum and energy information, the performance of replay speech answer-sheet detection can be significantly benefited. In order to improve the performance of replay speech answer-sheet detection, this paper proposes a method to decompose power spectrum in log scale into log frame-wise normalization spectrum (LFNS) and log spectral energy (LSE), both of them consider the characteristic of replay speech. It is the first contribution of the work.
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