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Recognition Routines

The speechtools package provides some routines of the type that are frequently used in automatic speech recognition applications.

Eventually, a complete set of components for building speech recognisers will be available. At this time, only N-gram language models are fully implemented and tested.

Acoustic modeling

At present there are two unfinished parts of the speech tools for acoustic modeling.

For decoding

The Acoustic_Model class provides a template for generalised acoustic modeling, and is used by the stack decoder (see section Decoding).

HMMs

The beginnings of a Hidden Markov Model class are available in the HMM class.

Language modeling

Ngram language models are supported by the EST_Ngrammar class (see section EST_Ngrammar C++ Class), and associated routines and programs. The programs described here supercede ngram described in section Executable Programs. As with all Speech Tools programs, the command line option -help prints a summary of options.

Building ngram language models

The program build_ngram estimates ngram language models from data. The data can be in any of the formats described in section Ngrammar data formats, and the language model saved in one of the formats described in section Ngrammar file formats.

Testing ngram language models

test_ngram computes language model entropy/perplexity on test data. At this time, only CSTR format (see section Ngrammar file formats) language models can be loaded.

Interpolating ngram language models

ch_ngram allows file format conversion and linear interpolation of ngram language models.

Decoding

A basic stack decoder template is implemented in the Decoder class. This uses template acoustic and language model classes, which are designed to be as general as possible (not limited to HMMs and N-grams for example).

This decoder may form the basis for future speech recognition work at CSTR.

A straightforward Viterbi decoder is already available, see (viterbi code description HERE), or see section Executable Programs.


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