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FONGBE-vm
LM
data
kaldi-scripts
README.md
run.sh

README.md

Fongbe Data collected by Fréjus A. A LALEYE

Prepared by Elodie Gauthier & Laurent Besacier

Université d’Abomey-Calavi, Bénin & GETALP LIG, Grenoble, France

OVERVIEW

The package contains Fongbe speech corpus with audio data in the directory data/. The data directory contains 3 subdirectories:

  1. train - speech data and transcription for training automatic speech recognition system (Kaldi ASR format [1])
  2. test - speech data and transcription (verified) for testing the ASR system (Kaldi ASR format)
  3. local - for now, contains the Fongbe vocabulary. Once you will ran the run.sh script it will contain the dict/ and lang/ directories needed to build the ASR system.

LM/ directory contains a text corpus and the language model.

PUBLICATION ON FONGBE SPEECH & LM DATA

More details on the corpus and how it was collected can be found on the following publication (please cite this bibtex if you use this data).
@inproceedings{laleye2016FongbeASR,
title={First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language Models},
author={A. A Laleye, Fréjus and Besacier, Laurent and Ezin, Eugène C. and Motamed, Cina},
year={2016},
organization={Federated Conference on Computer Science and Information Systems}}

SCRIPTS

In kaldi-scripts/ you will find:

  • 00_init_paths.sh - it initializes your PATH variable (NOTE: you have to modify this file by yourself)
  • 01_init_symlink.sh - it creates the symbolic links required to run the Kaldi scripts
  • 02_lexicon.sh - it creates the dict/ directory used by Kaldi
  • 03_lm_preparation.sh - it creates the lang/ directory used by Kaldi

WER RESULTS OBTAINED

(you should obtain the same on this data if you use the FONGBE-VM with Vagrant)
Acoustic models WER score (%) on test
Monophone (13 MFCC) 35.34
Triphone (13 MFCC) 27.42
Triphone (13 MFCC + delta + delta2) 26.75
Triphone (39 features) + LDA and MLLT 22.25
Triphone (39 features) + LDA and MLLT + SAT and FMLLR 17.77
Triphone (39 features) + LDA and MLLT + SGMM 16.57

REFERENCES

[1] KALDI: http://kaldi.sourceforge.net/tutorial_running.html