协作人工智能失联语音识别系统与数据增强使用生成对抗神经网络.
概括
这项研究引入了一种新的系统,用于改善失节性语言的自动语音识别 (ASR). 新的三阶段数据增强显著降低了文字错误率,增强了患有脱节症的人的沟通.
科学领域:
- 语音识别 语言识别
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 发病性发言对自动语音识别 (ASR) 系统构成重大挑战.
- 现有的ASR系统往往与失节性语言的可变性和可理解性降低作斗争.
- 提高失节性言语的ASR强度对于改善沟通可访问性至关重要.
研究的目的:
- 开发一种新的协作系统,用于将失关节性语音转换为非失关节性语音.
- 为了提高ASR系统的稳定性和准确性,为失节性言语进行微调.
- 为了提高自然性和可理解的沟通对于患有脱节症的个体.
主要方法:
- 采用了三阶段的数据增强框架,包括合成语音生成 (Tacotron2),节奏干扰和基于CycleGAN的转换与Inception-ResNet和时间掩盖.
- 该系统专注于绘制语音特征,同时保留语音结构和解决时间不规则.
- 使用UASpeech语音库进行了实验,以评估系统的性能.
主要成果:
- 与基线方法相比,拟议的系统显示出文字错误率 (WER) 的显著降低.
- 三阶段数据增强使WER在Wav2Vec2-XLSR模型中减少了9.81%,而在Whisper-Tiny模型中则减少了6.56%.
- 性能最好的系统平均获得了13.58%的WER,这表明准确性和自然性得到了改善.
结论:
- 这种新的协作框架有效地提高了失节性语音的语音识别的准确性和自然性.
- 三阶段的数据增强策略是系统成功的关键因素.
- 这个系统为患有脱节性关节症的人提供了一个有希望的解决方案,让他们更容易理解沟通.
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