The HTK Book Steve Young Gunnar Evermann Dan Kershaw ...

2.3 The Toolkit 18Labelled Utterancesth ih s ih s p iy t shsh t iy s z ih s ih thTranscriptionsth ih s ih s p iy t shsh t iy s z ih s ih thUnlabelled UtterancesHInitHRestHCompVHERestHHEdSub-WordHMMsFig. 2.3Training Sub-word HMMsOnce an initial set of models has been created, the tool HERest is used to perform embeddedtraining using the entire training set. HERest performs a single Baum-Welch re-estimation of thewhole set of HMM phone models simultaneously. For each training utterance, the correspondingphone models are concatenated and then the forward-backward algorithm is used to accumulate thestatistics of state occupation, means, variances, etc., for each HMM in the sequence. When all ofthe training data has been processed, the accumulated statistics are used to compute re-estimatesof the HMM parameters. HERest is the core HTK training tool. It is designed to process largedatabases, it has facilities for pruning to reduce computation and it can be run in parallel across anetwork of machines.The philosophy of system construction in HTK is that HMMs should be refined incrementally.Thus, a typical progression is to start with a simple set of single Gaussian context-independentphone models and then iteratively refine them by expanding them to include context-dependencyand use multiple mixture component Gaussian distributions. The tool HHEd is a HMM definitioneditor which will clone models into context-dependent sets, apply a variety of parameter tyingsand increment the number of mixture components in specified distributions. The usual processis to modify a set of HMMs in stages using HHEd and then re-estimate the parameters of themodified set using HERest after each stage. To improve performance for specific speakers thetools HEAdapt and HVite can be used to adapt HMMs to better model the characteristics ofparticular speakers using a small amount of training or adaptation data. The end result of whichis a speaker adapted system.The single biggest problem in building context-dependent HMM systems is always data insufficiency.The more complex the model set, the more data is needed to make robust estimates of itsparameters, and since data is usually limited, a balance must be struck between complexity andthe available data. For continuous density systems, this balance is achieved by tying parameterstogether as mentioned above. Parameter tying allows data to be pooled so that the shared parameterscan be robustly estimated. In addition to continuous density systems, HTK also supports