音声認識と機械学習   Speech Recognition and Machine Learning

文字サイズ 

担当教員
篠崎 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.

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