使用混合HMM/DNN嵌入式提取器模型在计算平行语言任务中.
Mercedes Vetráb1, Gábor Gosztolya1,2
1Institute of Informatics, University of Szeged, H-6720 Szeged, Hungary.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的计算双语学方法,它结合了自动语音识别和声学嵌入. 该方法有效地应对不同发言长度和小数据集的挑战,优于现有技术.
科学领域:
- 计算类平行语言学计算类平行语言学
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 计算类语言学分析非语言语音内容,用于诸如情感识别之类的任务.
- 关键的挑战包括处理可变的发言长度和有限的训练数据.
- 像x向量这样的现有方法在这些领域都有局限性.
研究的目的:
- 介绍一个新的特征提取方法,用于计算类 paralinguistics.
- 解决现有方法在处理不同发言长度和小语体方面的局限性.
- 为了提高跨多种类型的双语任务的表现.
主要方法:
- 一个混合隐藏马尔科夫模型/深度神经网络 (HMM/DNN) 声学模型被训练在自动语音识别 (ASR) 语料库上.
- 来自ASR模型的嵌入被用作 paralinguistic 任务的特征.
- 探索了五种聚合方法 (平均值,std dev,斜率,kurtosis,非零比率) 来得出发言级特征.
主要成果:
- 拟议的特征提取方法始终优于基线x向量的方法.
- 在各种 paralinguistic 任务中观察到性能增长.
- 结合聚合技术进一步增强了结果,这取决于任务和神经网络层.
结论:
- 提出的方法为计算类平行语言学提供了具有竞争力和资源效率的方法.
- 它有效地处理不同长度的发言和小 corpora.
- 该技术在远程监控和语音分析中的应用方面显示出前景.
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