- 担当教員
- 篠﨑 隆宏
- 使用教室
- 火1-2(G224)
- 単位数
- 講義：2 演習：0 実験：0
- 講義コード
- 88116
- シラバス更新日
- 2015年4月6日
- 講義資料更新日
- 2015年3月16日
- 学期
- 前期
講義概要
This course provides an introduction to computer based pattern recognition systems mainly focusing on speech signal processing. The organization of the systems, machine learning techniques, search algorithms, and performance evaluation methods are described.
講義の目的
The purpose of this lecture is to provide an introduction to speech recognition and machine learning. Not only fundamentals but also recent advances in the theory and practice of speech recognition techniques are explained.
講義計画
Speech analysis and feature extraction
Regression and clustering
Probabilistic distributions and maximum likelihood estimation
Gaussian mixture model (GMM)
Expectation maximization (EM) algorithm
Hidden Markov model (HMM)
Word network and N-gram model
Weighted finite state transducer (WFST)
Bayesian network
Speaker adaptation
Variational Bayes
Sampling
Boltzmann machine
Multilayer perceptron
Evolutionary algorithm
教科書・参考書等
Lecture notes will be handed out in class.
Reference:
* 「音声認識システム」 鹿野清宏 他 (オーム社)
* "Pattern Recognition and Machine Learning", C. M. Bishop (Springer)
関連科目・履修の条件等
basic understanding of linear algebra, differentials, probability and statistics
成績評価
Midterm and final exams.
