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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2010 >Age and Gender Recognition Based on Multiple Systems - Early vs. Late Fusion
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Age and Gender Recognition Based on Multiple Systems - Early vs. Late Fusion

机译：基于多种系统的年龄和性别识别-早期与晚期融合

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摘要

This paper focuses on the automatic recognition of a person's age and gender based only on his or her voice. Up to five different systems are compared and combined in different configurations: three systems model the speaker's characteristics in different feature spaces, i.e., MFCC, PLP, TRAPS, by Gaussian mixture models. The features of these systems are the concatenated mean vectors. System number 4 uses a physical two-mass vocal model and estimates in a data-driven optimization procedure 9 glottal features from voiced speech sections. For each utterance the minimum, maximum and mean vectors form a 27-dimensional feature vector. The last system calculates a 219-dimensional prosodic feature set for each utterance based on voice and unvoiced speech segments. We compare two different ways to fuse the different systems: First, we concatenate the system on feature level. The second way of combination is performed on score level by multi-class logistic regression. Despite there are just minor differences between the two approaches, late fusion is slightly superior. On the development set of the Interspeech Agender challenge we achieved an unweighted recall of 46.1 % with early fusion and 47.8% with late fusion.
机译：本文着重于仅根据他或她的声音自动识别其年龄和性别。最多可以对五个不同的系统进行比较并以不同的配置进行组合：三个系统通过高斯混合模型对不同特征空间（即MFCC，PLP，TRAPS）中说话人的特征进行建模。这些系统的特征是串联的均值向量。 4号系统使用了一个物理的两质量人声模型，并在数据驱动的优化程序中估计了9条声带语音部分的声门特征。对于每种话语，最小，最大和均值向量形成一个27维特征向量。最后一个系统根据语音和清语音段为每种话语计算219维韵律特征集。我们比较了两种不同的方法来融合不同的系统：首先，我们在功能级别上串联系统。组合的第二种方法是通过多类逻辑回归在得分级别上执行。尽管这两种方法之间只有很小的差异，但后期融合还是略胜一筹。在Interspeech Agender挑战的开发过程中，我们实现了早期融合的46.1％的未加权召回率和晚期融合的47.8％的未加权召回率。

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