引导式测验 · 硬 等级
Filterbank 和 PLP 功能测验
测试 log-mel 滤波器组和 PLP 如何将短语音帧转换为现代语音模型的感知形状特征。
问题 1 的 8
Why do speech filterbanks use mel- or Bark-spaced bands instead of uniformly spaced hertz?
预览每个问题8 问题
当您参加测验时,就会出现答案和解释。
- Why do speech filterbanks use mel- or Bark-spaced bands instead of uniformly spaced hertz?
- The scales allocate finer frequency spacing at low frequencies and coarser spacing at high frequencies, approximating aspects of hearing
- They make files smaller only
- They remove all background noise
- They guarantee perfect speech recognition
- PLP 中的线性预测步骤有何作用?
- 它增加了音量
- 它适合捕捉声道形状的平滑频谱包络
- 它增加了混响
- 它直接将音频转换为文本
- 哪种特征类型是现代深度语音识别模型的主要输入?
- 原始 MIDI 文件
- 手写成绩单
- Log-mel 滤波器组特征
- JPEG 频谱图
- MFCC 与原始 log-mel 滤波器组特征有何不同?
- MFCC 根本不使用过滤器
- MFCC 仅以分贝为单位进行测量
- MFCC 需要麦克风阵列
- MFCC 添加余弦变换以去相关滤波器组输出
- PLP 包含哪些基本梅尔滤波器组不包含的感知元素?
- 等响度曲线和立方根强度压缩
- 内置语言模型
- 自动翻译
- 立体声声像
- Why might a deep model use log-mel features or raw waveforms instead of PLP?
- PLP的使用是非法的
- The model can learn useful task-specific representations without every hand-designed PLP step
- 原始音频总是较小
- PLP仅适用于音乐
- What is a typical log-mel filterbank feature for one speech frame?
- A word-level translation
- A single pitch label for the whole recording
- The logarithm of spectral energy pooled through a mel-spaced filter
- A pixel color from a spectrogram image
- Which order best describes the main PLP processing steps?
- Translation, tokenization, and decoding
- Stereo panning, reverb, and compression
- Image segmentation followed by color correction
- Critical-band analysis, equal-loudness weighting, loudness compression, then linear prediction