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English (US) ARPA dictionary v3.0.0#

  • Maintainer: Montreal Forced Aligner

  • Language: English

  • Dialect: General American English

  • Phone set: ARPA

  • Number of words: 199,858

  • Phones: AA0 AA1 AA2 AE0 AE1 AE2 AH0 AH1 AH2 AO0 AO1 AO2 AW0 AW1 AW2 AY0 AY1 AY2 B CH D DH EH0 EH1 EH2 ER0 ER1 ER2 EY0 EY1 EY2 F G HH IH0 IH1 IH2 IY0 IY1 IY2 JH K L M N NG OW0 OW1 OW2 OY0 OY1 OY2 P R S SH T TH UH0 UH1 UH2 UW0 UW1 UW2 V W Y Z ZH

  • License: CC BY 4.0

  • Compatible MFA version: v3.0.0

  • 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}
}

Acoustic models
../../_images/full_logo_yellow.svg

Installation#

Install from the MFA command line:

mfa model download dictionary english_us_arpa

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](https://raw.githubusercontent.com/MontrealCorpusTools/mfa-models/main/dictionary/english/mfa/English (US) ARPA dictionary v3_0_0.dict).

Intended use#

This dictionary is intended for forced alignment of English transcripts.

This dictionary uses the ARPA phone set for English, and was used in training the English ARPA 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.

IPA Charts#

Consonants#

Obstruent symbols to the left of are unvoiced and those to the right are voiced.

Manner

Labial

Labiodental

Dental

Alveolar

Alveopalatal

Palatal

Velar

Glottal

Nasal
Occurrences:
44,929
Examples:
* sodom:
[S AA1 D AH0 M]
* misha:
[M IH1 SH AH0]
* wombs:
[W AA1 M Z]
* lamby:
[L AE1 M B IY0]
Occurrences:
95,743
Examples:
* lynch:
[L IH1 N CH]
* mon:
[M AA1 N]
* omens:
[OW1 M AH0 N Z]
* wayne:
[W EY1 N]
Occurrences:
15,307
Examples:
* gong:
[G AO1 NG]
* kung:
[K AH1 NG]
* spank:
[S P AE1 NG K]
* dung:
[D AH1 NG]

Stop
Occurrences:
32,646
Examples:
* unpin:
[AH0 N P AE1 N]
* pests:
[P EH1 S T S]
* gripe:
[G R AY1 P]
* peril:
[P EH1 R AH0 L]
Occurrences:
31,830
Examples:
* bolus:
[B OW1 L AH0 S]
* debut:
[D EY0 B Y UW1]
* verba:
[V EH1 R B AH0]
* baum:
[B AW1 M]
Occurrences:
76,538
Examples:
* feete:
[F IY1 T]
* pact:
[P AE1 K T]
* it':
[IH1 T AH0]
* blast:
[B L AE1 S T]
Occurrences:
52,829
Examples:
* docs:
[D AA1 K S]
* fated:
[F EY1 T IH0 D]
* reddy:
[R EH1 D IY0]
* cody:
[K OW1 D IY0]
Occurrences:
58,935
Examples:
* quilt:
[K W IH1 L T]
* crean:
[K R IY1 N]
* ankle:
[AE1 NG K AH0 L]
* cups:
[K AH1 P S]
Occurrences:
20,940
Examples:
* greg:
[G R EH1 G]
* galls:
[G AO1 L Z]
* gal:
[G AE1 L]
* bogs:
[B AA1 G Z]

Affricate
Occurrences:
8,472
Examples:
* catch:
[K AE1 CH]
* beach:
[B IY1 CH]
* chee:
[CH IY1]
* bench:
[B EH1 N CH]
Occurrences:
10,171
Examples:
* lige:
[L AY1 JH]
* bulgy:
[B AH1 L JH IY0]
* jules:
[JH UW1 L Z]
* jutes:
[JH UW1 T S]

Sibilant
Occurrences:
79,492
Examples:
* ibsen:
[IH1 B S AH0 N]
* syro:
[S AY1 R OW0]
* skye:
[S K AY1]
* vox:
[V AA1 K S]
Occurrences:
53,319
Examples:
* pepys:
[P EH1 P IY0 Z]
* ogres:
[OW1 G ER0 Z]
* tease:
[T IY1 Z]
* leos:
[L IY1 OW0 Z]
Occurrences:
12,390
Examples:
* shuns:
[SH AH1 N Z]
* shred:
[SH R EH1 D]
* shone:
[SH OW1 N]
* short:
[SH AO1 R T]
Occurrences:
798
Examples:
* rouge:
[R UW1 ZH]
* dijon:
[D IY0 ZH OW1 N]
* asia:
[EY1 ZH AH0]
* usual:
[Y UW1 ZH UW0 AH0 L]

Fricative
Occurrences:
21,273
Examples:
* fishy:
[F IH1 SH IY0]
* faint:
[F EY1 N T]
* farn:
[F AA1 R N]
* flaws:
[F L AO1 Z]
Occurrences:
17,654
Examples:
* voted:
[V OW1 T AH0 D]
* vince:
[V IH1 N S]
* avast:
[AA0 V AA0 S T]
* verds:
[V ER1 D Z]
Occurrences:
6,636
Examples:
* thole:
[TH OW1 L]
* goth:
[G AA1 TH]
* thees:
[TH IY1 Z]
* ninth:
[N AY1 N TH]
Occurrences:
1,328
Examples:
* thee:
[DH IY1]
* tha:
[DH AH0]
* these:
[DH IY1 Z]
* that:
[DH AE1 T]
Occurrences:
13,794
Examples:
* sahib:
[S AH0 HH IH1 B]
* hagar:
[HH EY1 G AA0 R]
* hegel:
[HH EH1 G AH0 L]
* heads:
[HH EH1 D Z]

Approximant
Occurrences:
15,035
Examples:
* wynn:
[W IH1 N]
* we'll:
[W IY1 L]
* weber:
[W EH1 B ER0]
* quiet:
[K W AY1 AH0]
Occurrences:
71,955
Examples:
* eery:
[IH1 R IY0]
* yuruk:
[Y UH1 R IH0 K]
* farms:
[F AA1 R M Z]
* maure:
[M AO1 R]
Occurrences:
8,242
Examples:
* yella:
[Y EH1 L AH0]
* yuki:
[Y UW1 K IY0]
* fuses:
[F Y UW1 Z IH0 Z]
* hugi:
[HH Y UW1 G IY0]

Lateral
Occurrences:
77,647
Examples:
* graal:
[G R AA1 L]
* lab:
[L AE1 B]
* allee:
[AH0 L IY1]
* oral:
[AO1 R AH0 L]

Vowels#

Vowel symbols to the left of are unrounded and those to the right are rounded.

Front

Near-Front

Central

Near-Back

Back

Close
Occurrences:
55,493
Examples:
* willy:
[W IH1 L IY0]
* leeks:
[L IY1 K S]
* mie's:
[M IY0 Z]
* quasi:
[K W AA1 S IY0]
Occurrences:
16,342
Examples:
* to't:
[T UW0 T]
* ute:
[Y UW1 T]
* danu:
[D AA1 N UW0]
* chiku:
[CH IY1 K UW2]
Occurrences:
80,466
Examples:
* arnim:
[AA1 R N IH2 M]
* orbis:
[AO1 R B IH0 S]
* nitro:
[N IH1 T R OW0]
* indra:
[IH2 N D R AH0]
Occurrences:
4,053
Examples:
* good':
[G UH1 D]
* tush:
[T UH1 SH]
* would:
[W UH1 D]
* curae:
[K Y UH0 R EY1]

Close-Mid
Occurrences:
21,451
Examples:
* dumay:
[D AH0 M EY0]
* jabe:
[JH AA1 B EY0]
* melee:
[M EY1 L EY2]
* padre:
[P AE1 D R EY2]
Occurrences:
25,982
Examples:
* hondo:
[HH AA1 N D OW0]
* sligo:
[S L AY1 G OW2]
* gueux:
[G OW1]
* tiago:
[T IY0 AA1 G OW0]
Occurrences:
118,104
Examples:
* juxta:
[JH AH2 K S T AH0]
* bason:
[B AE1 S AH0 N]
* dully:
[D AH1 L IY0]
* lyon:
[L AY1 AH0 N]

Open-Mid
Occurrences:
39,991
Examples:
* harim:
[HH EH0 R IY1 M]
* wor:
[D AH1 B EH0 L Y UW1 OW1 AA1 R]
* toque:
[T AA2 K W EH0]
* pepin:
[P EH1 P IH0 N]
Occurrences:
34,399
Examples:
* boaz:
[B OW1 AE0 Z]
* altai:
[AE0 L T AY1]
* kodak:
[K OW1 D AE2 K]
* kat's:
[K AE2 T S]
Occurrences:
41,063
Examples:
* curds:
[K ER1 D Z]
* serle:
[S ER1 AH0 L]
* farer:
[F EH2 R ER0]
* rigor:
[R IH1 G ER0]

Open
Occurrences:
38,981
Examples:
* ameer:
[AA2 M IH1 R]
* azara:
[AA0 Z AA1 R AH0]
* tom's:
[T AA1 M Z]
* aesop:
[IY1 S AA2 P]
Occurrences:
18,102
Examples:
* kiosk:
[K IY1 AO2 S K]
* wharf:
[HH W AO1 R F]
* henri:
[AO2 R IY1]
* utah:
[Y UW1 T AO2]

Diphthongs#

  • AW

  • AY

  • OY

Stress#

  • 0

  • 1

  • 2

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