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首页> 外文期刊>Circuits, systems, and signal processing >Wavelet-Based Power Normalized Spectrum for Hindi Phoneme Classification
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Wavelet-Based Power Normalized Spectrum for Hindi Phoneme Classification

机译：基于小波的功率归一化谱用于印地语音素分类

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This paper presents wavelet-based power normalized spectrum for computing robust cepstral features named WP-PNCC features. The proposed technique computes wavelet packet-based short-time spectrum of speech signal. A nonlinear function is defined as relating power spectrum of clean speech to the power spectrum of speech corrupted with noise. The constants of function are computed from longer-duration speech spectrum, and the short-time spectrum for each frame is weighted with the power function. The weighted speech spectrum is processed with logarithmic and discrete cosine transform operation to compute cepstral coefficients. The cepstral coefficients thus obtained are processed with quantile-based cepstral dynamics normalization technique. The proposed features are examined with hidden Markov model classifier on TIFR database for Hindi phoneme classification task and on TIMIT database for English phoneme classification task along with mel-frequency cepstral coefficients, power normalized cepstral coefficients and 24-band wavelet-based features in clean and noisy environments. Different noises from NOISEX-92 database are used for preparing noisy database with SNR ranging from 20 dB to 0 dB. The results show enhanced performance of proposed features in all the considered cases. The simulations are performed on MATLAB 2015b. The performance of proposed features is also evaluated on hidden Markov model toolkit-based speech recognition system. The comparative results confirm the robustness of proposed features with sufficient improvement over other features examined in this paper.
机译：本文提出了基于小波的功率归一化频谱，用于计算鲁棒的倒谱特征，称为WP-PNCC特征。所提出的技术计算基于小波包的语音信号的短时频谱。非线性函数被定义为将干净语音的功率谱与被噪声破坏的语音的功率谱相关。从较长的语音频谱中计算出函数常数，并使用幂函数对每帧的短时频谱进行加权。用对数和离散余弦变换运算处理加权语音频谱，以计算倒频谱系数。这样获得的倒谱系数用基于分位数的倒谱动力学归一化技术处理。使用隐藏的马尔可夫模型分类器在TIFR数据库中对印地语音素分类任务和TIMIT数据库中的隐式马尔可夫模型分类器进行了检验，并在纯音和纯谱中使用了梅尔频率倒谱系数，功率归一化倒谱系数和基于24波段小波的特征。嘈杂的环境。来自NOISEX-92数据库的不同噪声用于准备SNR为20 dB至0 dB的嘈杂数据库。结果表明，在所有考虑的情况下，所提出功能的性能均得到增强。仿真在MATLAB 2015b上执行。在基于隐马尔可夫模型工具箱的语音识别系统上还评估了提出的功能的性能。比较结果证实了所提出功能的鲁棒性，与本文所研究的其他功能相比有足够的改进。

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