Tag Archives: machine learning

Using the grapheme-to-phoneme feature in CMU Sphinx-4

Foreword This article summarizes and updates the previous articles [1] related to the new grapheme-to-phoneme (g2p) feature in CMU Sphinx-4 speech recognizer [2]. In order to support automatic g2p transcription in Sphinx-4 there were created a new weighted finite state transducers (wfst) in java [3] which its current API will be presented in a future… Read More »

Automating the creation of joint multigram language models as WFST: Part 2

(originally posted at http://cmusphinx.sourceforge.net/2012/06/automating-the-creation-of-joint-multigram-language-models-as-wfst-part-2/) Foreword This a article presents an updated version of the model training application originally discussed in [1], considering the compatibility issues with phonetisaurus decoder as presented in [2]. The updated code introduces routines to regenerate a new binary fst model compatible with phonetisaurus’ decoder as suggested in [2] which will be… Read More »

Compatibility issues using binary fst models generated by OpenGrm NGram Library with phonetisaurus decoder

(originally posted at http://cmusphinx.sourceforge.net/2012/06/compatibility-issues-using-binary-fst-models-generated-by-opengrm-ngram-library-with-phonetisaurus-decoder/) Foreword Previous articles have shown how to use OpenGrm NGram Library for the encoding of joint multigram language models as WFST [1] and provided the code that simplifies and automates the fst model training [2]. As described in [1] the generated binary fst models with the procedures described in those articles… Read More »

Automating the creation of joint multigram language models as WFST

Notice: This article is outdated. The application described here is now part of the SphinxTrain application. Please refer to recent articles in CMUSphinx category for the latest info. (originally posted at http://cmusphinx.sourceforge.net/2012/06/automating-the-creation-of-joint-multigram-language-models-as-wfst/) Foreword Previous articles have introduced the C++ code to align a pronounciation dictionary [1] and how this aligned dictionary can be used in… Read More »

Using OpenGrm NGram Library for the encoding of joint multigram language models as WFST

(originally posted at http://cmusphinx.sourceforge.net/2012/06/using-opengrm-ngram-library-for-the-encoding-of-joint-multigram-language-models-as-wfst/) Foreword This article will review the OpenGrm NGram Library [1] and its usage for language modeling in ASR. OpenGrm makes use of functionality in the openFST library [2] to create, access and manipulate n-gram language models and it can be used as the language model training toolkit for integrating phonetisaurus’ model… Read More »

Porting phonetisaurus many-to-many alignment python script to C++

Notice: This article is outdated. The application described here is now part of the SphinxTrain application. Please refer to recent articles in CMUSphinx category for the latest info. (originally posted at http://cmusphinx.sourceforge.net/2012/05/porting-phonetisaurus-many-to-many-alignment-python-script-to-c/) Foreword Following our previous article on phonetisaurus [1] and the decision to use this framework as the g2p conversion method for my GSoC… Read More »

Phonetisaurus: A WFST-driven Phoneticizer – Framework Review

Foreword This article tries to analyze the phonetisaurus g2p [1], [2] code by describing it’s main parts and algorithms behind these. Phonetisaurus is a modular system and includes support for several third-party components. The system has been implemented primarily in python, but also leverages the OpenFST framework [3]. 1. Overall Architecture The procedure for model… Read More »

Letter to Phoneme Conversion in CMU Sphinx-4: Literature review

1. Foreword Currently Sphinx-4 uses a predefined dictionary for mapping words to sequence of phonemes. I propose modifications in the Sphinx-4 code that will enable it to use trained models (through some king of machine learning algorithm) to map letters to phonemes and thus map words to sequence of phonemes without the need of a… Read More »

Implementation of Elman Recurrent Neural Network in WEKA

Foreword In this article, we will discuss the implementation of the Elman Network or Simple Recurrent Network (SRN) [1],[2] in WEKA. The implementation of Elman NN in WEKA is actually an extension to the already implemented Multilayer Perceptron (MLP) algorithm [3], so we first study MLP and it’s training algorithm, continuing with the study of… Read More »

Implementation of Competitive Learning Networks for WEKA

Foreword In a previous article, we shown that by using WEKA a researcher can easily implement her own algorithms without other technical concernings like binding an algorithm with a GUI or even loading the data from a file/database, as these tasks and many others are handled transparently by the WEKA framework. [1] In this article… Read More »