人类语音使用低频和高频能量的索引和细分特征的分类)
Jeremy J Donai1, D Dwayne Paschall2, Saad Haider3
1Department of Speech, Language, and Hearing Sciences, Texas Tech University Health Sciences Center, Lubbock, Texas 79430, USA.
The Journal of the Acoustical Society of America
|November 16, 2023
概括
高频 (4kHz以上) 语音信息保留了关键的索引细节,用于分类语音特征,如说话者的身份和性别,即使低频率被删除. 这一发现凸显了机器学习应用中全语音频谱的重要性.
科学领域:
- 声学语音学的声音学
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 高频话语频谱 (4-5 kHz以上) 包含了重要的信息.
- 以前的研究已经确定了这个光谱区域的实用性.
研究的目的:
- 为了比较低频和高频语音区域的索引和细分信息.
- 评估机器学习在使用不同频段对语音方面进行分类方面的有效性.
主要方法:
- 从20个说话者的元音段被分析使用时间字典合奏模型.
- 语音信号在未过,低通过 (4kHz以下),高通过 (4kHz以上) 条件下进行了测试.
主要成果:
- 在未过和低通过条件下,对元音分类,说话者的性别和身份的分类准确度超过了90%.
- 高通过信号 (4kHz以上) 也产生了机会以上的分类性能.
- 高通过对说话者的性别和身份分类的影响最小,表明保存的索引信息.
结论:
- 高频发言区域 (4kHz以上) 保留了大量的索引信息.
- 机器学习模型可以有效地利用高频语音组件进行扬声器和语音分类.
- 了解语音频段实用性对于高级语音处理和识别系统至关重要.
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