本课程在教学设计上主要采用课堂教学和课后实践相结合的方式,教师讲授以多媒体教学为基础,配备中英文课件以及丰富音视频资料,学生课后设计涵盖编程实践和文献调研,提升学生自主学习能力。本课程研究生教学学习过程中进行分组交流和讨论,针对课程相关技术点设计案例并建立学习小组,课堂上引导小组进行相互交流和研讨,通过课程提问、学生互相提问以及针对各自科研领域 结合课程内容进行创新性发问、批判性发问等方式,引导学生理论联系实际,并能灵活运用课程知识和各自科研领域融会贯通,通过多角度提问的形式活跃课程氛围,促进知识点的吸收和理解。 本课程引入国外一流名校相关课程先进教学经验,进一步完善课件、设计、 研讨等环节的设置,培养学生使用英语进行学习与交流的能力,包括书面与口头表达能力。课程案例以及实验内容基于开源软件,加强科研实践,培养研究生创 新意识与动手能力。 本课程充分利用已有的研究生创新平台以及语音室等软硬件资源,面向课程 相关研究生开展创新实验实践活动,安排学生在实际应用中进行体验,从用户体 验角度提出自己的问题和想法,从而提高对技术深度思考的能力。 本课程通过课堂讲授、分组研讨、专家讲座、编程实践、英文交流等全方位、 多角度的教学设计与实践,激发学生学习兴趣和主动性,培养研究生创新性、批 判性、颠覆性思维,并恰当采用多媒体、网络等现代教学技术、方法和手段,显著提高教学效果。
Overview
Syllabus
- 1. Introduction
- 1.1 Outline
- 1.2 Application of digital speech signal processing
- 1.3 Development history of speech signal processing
- 1.4 Basic Knowledge of speech signal
- Test
- 2. Fundamentals of Speech processing
- 2.1 Speech processing techniques
- 2.2 Short-time analysis and windowing
- 2.3 Short-time parameters of speech signal
- 2.4 Pitch estimation
- Test
- 3. Speech production and perception
- 3.1 Speech chain
- 3.2 Speech production
- 3.3 Speech perception
- 3.4 Speech quality test
- Test
- 4. Linear predictive analysis(LPA)
- 4.1 Basic concepts of LP
- 4.2 Estimation of LP coefficients
- 4.3 LP spectrum and basic features
- 4.4 Alternative Representations of LP parameters
- Test
- 5. Homomorphic analysis(HA)
- 5.1 Homomorphic systems
- 5.2 Cepstrum and complex cepstrum
- 5.3 Homomorphic filtering
- 5.4 Homomorphic analysis of speech signal
- Test
- 6. Vector quantization(VQ)
- 6.1 Basic concept of VQ
- 6.2 VQ system design
- 6.3 Codebook design algorithms
- 6.4 Different types of VQ
- Test
- 7. Hidden Markov Models(HMM)
- 7.1 Prerequisite knowledge
- 7.2 Basic concept of HMM
- 7.3 Three basic problems of HMM
- 7.4 Insights of HMM
- Test
- 8. Speech Coding
- 8.1 Overview of speech coding
- 8.2 Speech coding strategies
- 8.3 Code Excited Linear Prediction (CELP)
- 8.4 Key technologies of speech coding
- Test
- 9. Speech Recognition
- 9.1 Overview of speech recognition
- 9.2 Basic speech recognition systems
- 9.3 Models in speech recognition
- 9.4 Challenges for speech recognition
- Test
- 10. Speech Enhancement
- 10.1 Overview of speech enhancement
- 10.2 Speech separation problem
- 10.3 Monaural speech denoising
- 10.4 Other speech enhancement techniques
- Test
- 11. Speech Synthesis
- 11.1 Overview of speech synthesis
- 11.2 Methods of speech synthesis(PartI)
- 11.3 Methods of speech synthesis(PartII)
- 11.4 Prosody and PSOLA
- Test
- 12. Latest development in speech signal processing
- 12.1 Background of Deep Learning
- 12.2 Outline of neural networks
- 12.3 Deep learning based speech recognition
- 12.4 Deep learning based speech separation
- Test
- Exam
Taught by
Jin Wang