{title}
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Dictionary details
- Maintainer: {maintainer}
- Language: {language_link}
- Dialect: {dialect_link}
- Phone set: {phone_set_link}
- Number of words:
{word_count:,} - Phones:
{phones} - License: {license_link}
- Compatible MFA version:
v{mfa_version} - Citation:
{citation}
- 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 model download dictionary {model_name}
Or download from the release page.
The dictionary available from the release page and command line installation has pronunciation and silence probabilities estimated as part acoustic model training (see Silence probability format and training pronunciation probabilities for more information. If you would like to use the version of this dictionary without probabilities, please see the plain dictionary.
Intended use
This dictionary is intended for forced alignment of {language_link} transcripts.
This dictionary uses the {phone_set_link} phone set for {language}, and was used in training the {language} {phone_set_link} 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 seen 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.