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首页> 外文会议>European Conference on Speech Communication and Technology v.3; 20010903-20010907; Aalborg; DK >Feature Extraction from Time-Frequency matrices for Robust Speech Recognition
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Feature Extraction from Time-Frequency matrices for Robust Speech Recognition

机译：从时频矩阵中提取特征以实现鲁棒的语音识别

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摘要

In this paper we present a study about time-frequency distribution of acoustic-phonetic information for the Spanish language. This is based on a large Spanish database automatically labeled, and we conclude that results are similar to those obtained for hand-labeled english databases. We use bidimensional LDA to extract discriminant features in time-frequency domain (TF) that are more robust in noise than the standard ones based on MFCC and time derivatives. We show that TF domain and its corresponding transformed domain (CTM) are equivalent from the point of view of LDA analysis and use this fact to reduce the dimensionality of the problem. Finally, cascade unidimensional LDA (CLDA) is applied first in frequency and then in time. This gives better estimates of projection vectors and better recognition performance. The proposed techniques are evaluated in a connected digit recognition task. Utterances have been artificially corrupted with additive real noises.
机译：在本文中，我们对西班牙语的语音信息的时频分布进行了研究。这是基于大型西班牙文数据库的自动标记，我们得出的结论是，结果与手工标记的英语数据库相似。我们使用二维LDA提取时频域（TF）中的判别特征，这些特征在噪声方面比基于MFCC和时间导数的标准特征要强。从LDA分析的角度来看，我们表明TF域及其对应的转换域（CTM）是等效的，并使用此事实来减少问题的维数。最后，级联一维LDA（CLDA）首先应用于频率，然后应用于时间。这样可以更好地估计投影矢量，并获得更好的识别性能。所提出的技术在连接数字识别任务中进行评估。话语已经被附加的真实噪声人为地破坏了。

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