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On the Use of the Formant Features in the Dynamic Time Warping Based Recognition of Isolated Words

机译:共振峰特征在基于动态时间规整的孤立词识别中的应用

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摘要

A possibility lo use the formant features (FF) in the user-dependent isolated word recognition has been investigated. The word recognition was performed using a dynamic time-warping technique. Several methods of the formant feature extraction were compared and a method based on the singular prediction polynomials has been proposed for the recognition of isolated words. Recognition performance of the proposed method was compared to that of the linear prediction coding (LPC) and LPC-derived cepstral features (LPCC). In total, 111 Lithuanian words were used in the recognition experiment. The recognition performance was evaluated at various noise levels. The experiments have shown that the formant features calculated from the singular prediction polynomials arc more reliable than the LPC and LPCC features at all noise levels.
机译:已经研究了在依赖用户的孤立单词识别中使用共振峰特征(FF)的可能性。使用动态时间扭曲技术执行单词识别。比较了共振峰特征提取的几种方法,并提出了一种基于奇异预测多项式的孤立词识别方法。将该方法的识别性能与线性预测编码(LPC)和LPC衍生的倒谱特征(LPCC)进行了比较。在识别实验中,总共使用了111个立陶宛语单词。在各种噪声水平下对识别性能进行了评估。实验表明,在所有噪声水平下,由奇异预测多项式计算出的共振峰特征都比LPC和LPCC特征更可靠。

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