Natural Language Processing · 免费预览
自动语音识别
将波形映射为文本:卷积前端、注意力编码器-解码器(如 Whisper)、自监督 HuBERT、CTC 损失及词错误率。
1. Automatic Speech Recognition
Automatic Speech Recognition
2. Convolutional front ends
Convolutional front ends
3. Encoder-decoder and self-supervised recognition
Encoder-decoder and self-supervised recognition
4. CTC alignment and error rates
CTC alignment and error rates
5. Ranking the settings
Which of these recognition settings should have the highest word error rate for a system trained on standard read speech?
6. Counting encoder frames
Whisper's 30-second audio context yields 3,000 log mel frames at one per 10 ms, and its second convolutional layer uses a stride of 2. How many embeddings per context window leave the two-layer front end?
7. Scoring a transcript
Against a -word reference, a recognizer produces a hypothesis with substitutions, insertions and deletions. What is its word error rate, to three decimal places?
8. Inventing the targets
In the first stage of HuBERT pretraining, what serves as the gold target at each masked frame?
9. Collapsing a path
A CTC decoder emits the frame-level path [·, a, b, b, ·, b, a] over alphabet {a, b, ·} with · the blank. Apply the collapsing function B, merging consecutive duplicates then deleting blanks. What text does the path decode to?