简介

这是台大的李琳山老师的课程，课程前八节是基础，后八节课是拓展。

PartI: Fundamental Topics

1.0 Introduction to Digital Speech Processing

Acoustic Models ：声学模型，语音波形->语音单位（比如音素）

Lexicon：词典，语音单位->语言单位（比如字，或者单词或者词语）

Language Model：语言模型，比如预测在前一个语言单位下后一个语言单位的概率

2.0 Fundamentals of Speech Recognition

HMM

定义如下：

N-dim Guassion

Feature Extraction (Front-end Signal Processing)

Pre-emphasis

Endpoint Detection (Speech/Silence Discrimination)

Windowing

Rectangular window

Hamming window

MFCC

N-gram

MAP(最大后验概率)

3.0 Map of Subject Areas

4.0 More about Hidden Markov Models

5.0 Acoustic Modeling

6.0 Language Modeling

7.0 Speech Signals and Front-end Processing

8.0 Search Algorithms for Speech Recognition

Part II: Advanced Topics

9.0 Speech Recognition Updates

10.0 Speech-based Information Retrieval

11.0 Spoken Document Understanding and Organization for User-content Interaction

12.0 Computer-assisted Language Learning(Call)

13.0 Speaker Variabilities: Adaption and Recognition

14.0 Latent Topic Analysis

15.0 Robustness for Acoustic Environment

16.0 Some Fundamental Problem-solving Approaches

17.0 Spoken Dialogues

18.0 Conclusion

语者识别与适应 SI 语者独立 SD 语者相关 SA 语者适应 MAP 最大后验概率 MLLR 最大概率线性压缩 PCA 主成分分析 EigenVoice (PAC) SAT CAT