french_prosodylab v2.0.0
French PROSODYLAB dictionary v2.0.0
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Dictionary details
- Maintainer: Montreal Forced Aligner
- Language: French
- Dialect: N/A
- Phone set: PROSODYLAB
- Number of words:
125,373 - Phones:
@ E G N O R S Z ^ a b cinq d deux e f g huit i j k l m n neuf o p s t to u un v w x y z - License: CC BY 4.0
- Compatible MFA version:
v2.0.0 - Citation:
@article{
gorman2011prosodylab,
author={Gorman, Kyle and Howell, Jonathan and Wagner, Michael},
title={Prosodylab-aligner: A tool for forced alignment of laboratory speech},
journal={Canadian Acoustics},
volume={39},
number={3},
pages={192--193},
year={2011}
}- If you have comments or questions about this dictionary or its phone set, you can check previous MFA model discussion posts or create a new one.
Installation
Install from the MFA command line:
mfa models download dictionary french_prosodylab
Or download from the release page.
Intended use
This dictionary is intended for forced alignment of French transcripts.
This dictionary uses the PROSODYLAB phone set for French, and was used in training the French PROSODYLAB acoustic model. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
Performance Factors
When trying to get better alignment accuracy, adding pronunciations is generally helpful, especially for different styles and dialects. The most impactful improvements will generally be felt when adding reduced variants that involve deleting segments/syllables common in spontaneous speech. Alignment must include all phones specified in the pronunciation of a word, and each phone has a minimum duration (by default 10ms). If a speaker pronounces a multisyllabic word with just a single syllable, it can be hard for MFA to fit all the segments in, so it will lead to alignment errors on adjacent words as well.
Ethical considerations
Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
Demographic Bias
You should assume every machine learning model has demographic bias unless proven otherwise. For pronunciation dictionaries, it is often the case that transcription accuracy and lexicon coverage for the prestige variety modeled in this dictionary compared to other variants. If you are using this dictionary in production, you should acknowledge this as a potential issue.