引导式测验 · 中等 等级
语音测验的文本规范化
测试语音文本规范化：扩展数字和符号、对标记进行分类、解析上下文以及使用规则或神经模型。
问题 1 的 8
语音文本规范化主要做什么？
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- 语音文本规范化主要做什么？
- 将书面文字重写为完全说出来的单词
- 压缩音频文件
- 将文本翻译成另一种语言
- 检测说话者的情绪
- 一个好的系统应该如何规范语音的“5 美元”？
- ‘五块钱’
- “五美元”
- '五'
- ‘百分之五’
- 为什么要规范化“Dr.”这样的标记还是“IV”棘手？
- 这些标记总是以相同的方式发音
- 它们不包含字母
- 它们的正确扩展取决于上下文
- 它们仅用于数学
- What data did the cited neural text-normalization study use to evaluate its models?
- Unlabeled phone audio only
- Medical records annotated by clinicians
- A small set of manually written code snippets
- English and Russian Wikipedia sentences verbalized by Google TTS’s Kestrel system
- What safeguard against unrecoverable errors does the cited neural text-normalization paper discuss?
- WFST-based covering grammars
- Removing the reference transcript
- Replacing all token classes with a speech waveform
- Using image segmentation at inference
- 在经典规范化中，为标记分配“符号类别”的作用是什么？
- 它选择说话者的声音
- 它标记标记（例如日期、金钱、度量），以便应用正确的语言表达器
- 它压缩文本
- 它删除所有标点符号
- In the Kestrel TTS normalization system, what follows tokenization and classification?
- A speaker diarization pass labels voices
- A vocoder renders the waveform
- A verbalization stage applies grammars to turn classified tokens into spoken forms
- An image model recognizes the page layout
- Why can English and Russian text require different normalization grammars?
- Every language uses identical number and date formats
- Language has no effect on spoken forms
- Russian text cannot be normalized for speech
- Written conventions and verbalization rules vary by language