## Statistical Methods for Speech RecognitionThis book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques. |

### What people are saying - Write a review

This is a fantastic book because it succinctly lays out many of the fundamentals of the field. I find myself repeatedly turning to it because I know it's one place where I'll find a clear and precise explanation of many basic concepts and algorithms that are standard in the field today.

Every time I look at this book it reminds me what a great loss Fred's passing was -- and continues to be -- for all of us in the field today.

Mark Johnson

### Contents

Chapter | 4 |

Chapter | 7 |

Chapter | 12 |

Chapter | 15 |

Transitions | 23 |

References | 37 |

Basic Language Modeling | 45 |

Chapter 13 | 50 |

System | 142 |

Dimensions | 158 |

Language Modeling | 166 |

Chous Method | 179 |

Based on Word Encoding | 184 |

Data | 190 |

Chapter 11 | 197 |

Voting | 234 |

History | 59 |

The Viterbi Search | 72 |

State Spaces | 86 |

Recognition | 97 |

Shortcuts | 109 |

Entropy | 119 |

Theorem | 126 |

Adaptation | 248 |

Estimation of Probabilities from Counts | 257 |

Estimate | 263 |

of GoodTuring Estimation | 269 |

275 | |