The HTK Book (for HTK Version 3.2.1)

Young et al., 2002

Chapter 1The Fundamentals of HTK

HTK is a toolkit for building hidden Markov models (HMMs).

Primarily used to build ASRs, but also other HMM systems: speaker and image recognition, automatic text summarization etc.

HTK has tools (modules) for both training and testing HMM systems.

How to Train and Test an ASR?

Things needed: A labeled speech corpus and a dictionary (+ grammar).

Procedure: 1. Divide corpus into training, development and test sets. 2. Train acoustic models. 3. Test, retrain, test … on the

development set. 4. Test on the test data.

How to Build an ASR Using HTK?

Goal: A recognizer for voice dialing.

( SENT-START ( DIAL <$digit> | (PHONE|CALL) $name) SENT-END )

Creating a Dictionary

HDMan a list of the phones. An HMM will be estimated for each of these phones.

Recording the Data

HSLab noname HSGen (wdnet dict) testprompts

Transcribing the Data

HMM training is supervised learning.

Coding the Data

HTK supports frame-based FFTs, LPCs, MFCCs, user-defined etc.

Output Probability Specification

Most common one is CDHMM. HTK also allows discrete probabilities (for

VQ data).

Flat Start Training

Build a prototype HMM with reasonable initial guesses of its parameters (HCompV).

Specify the topology – usually left to right and 3 states w/ no skips.

Create a MMF. Now use HRest or HERest for

training.

Realigning and Creating Triphones.

Use pseudo-recognition to force align training data w/ multiple pronunciations.

Evaluation

Other Issues

HTK supports supervised and unsupervised speaker adaptation (HVite).

Language model: n-gram language models.