English MFA dictionary v2.2.1#

  • Maintainer: Montreal Forced Aligner

  • Language: English

  • Dialect: N/A

  • Phone set: MFA

  • Number of words: 95,278

  • Phones: a aj aw b c d e ej f h i j k kp l m n o ow p s t u v w z æ ç ð ŋ ɐ ɑ ɑː ɒ ɒː ɔ ɔj ə əw ɚ ɛ ɛː ɜ ɜː ɝ ɟ ɟʷ ɡ ɡb ɡʷ ɪ ɫ ɫ̩ ɲ ɹ ɾ ɾʲ ɾ̃ ʃ ʉ ʉː ʊ ʎ ʒ ʔ θ

  • License: CC BY 4.0

  • Compatible MFA version: v2.1.0

  • Citation:

@techreport{mfa_english_mfa_dictionary_2023,
	author={McAuliffe, Michael and Sonderegger, Morgan},
	title={English MFA dictionary v2.2.1},
	address={\url{https://mfa-models.readthedocs.io/pronunciation dictionary/English/English MFA dictionary v2_2_1.html}},
	year={2023},
	month={May},
}
../../_images/full_logo_yellow.svg

Installation#

Install from the MFA command line:

mfa model download dictionary english_mfa

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 MFA dictionary v2_2_1.dict).

Intended use#

This dictionary is intended for forced alignment of English transcripts.

This dictionary uses the MFA phone set for English, and was used in training the English MFA 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:
39,779
Examples:
* hon:
[h m ɔ n]
* pomp:
[ ɔ m p]
* limer:
[l aj m ə]
* scams:
[s k a m z]
Occurrences:
8,153
Examples:
* sami:
[s ɑː i]
* mute:
[ t]
* foamy:
[f ow i]
* ameba:
[a b a]
Occurrences:
3
Examples:
Occurrences:
77,890
Examples:
* haven:
[ç e v ɛ n]
* hench:
[h ɛ n ]
* jeans:
[ n z]
* joned:
[ ow n d]
Occurrences:
72
Examples:
Occurrences:
15,838
Examples:
* bonny:
[b ɑ ɲ i]
* dini:
[ ɲ i]
* handy:
[h a ɲ i]
* joni:
[ ow ɲ i]
Occurrences:
15,459
Examples:
* huang:
[w ɑ ŋ]
* fling:
[f ʎ i ŋ ɡ]
* angst:
[a ŋ k s t]
* ingle:
[ɪ ŋ ɡ ə ɫ]

Stop

Occurrences:
20,995
Examples:
* shlep:
[ʃ l ɛ p]
* aspis:
[a s p ɪ s]
* scrip:
[s c ɹ i p]
* helps:
[h ɛ ɫ p s]
Occurrences:
25,566
Examples:
* medb:
[m ɛ b]
* back:
[b æ k]
* banat:
[b ə ɲ æ t]
* bogie:
[b ow ɟ i]
Occurrences:
98
Examples:
Occurrences:
103
Examples:
Occurrences:
52,344
Examples:
* doped:
[d ow p t]
* vitro:
[ t ɹ ow]
* mott:
[m ɑ t]
* octa:
[ɒ k t ə]
Occurrences:
39,638
Examples:
* maud:
[m ɒː d]
* dust:
[d ɐ s t]
* roid:
[ɹ ɔj d]
* fraud:
[f ɹ ɒ d]
Occurrences:
9,117
Examples:
* mcrae:
[m ə c ɹ ej]
* tacky:
[ æ c i]
* cleef:
[c ʎ f]
* skink:
[s c ɪ ŋ k]
Occurrences:
4,540
Examples:
* ogry:
[əw ɟ ɹ i]
* bogey:
[b ow ɟ i]
* pogey:
[ o ɟ i]
* grep:
[ɟ ɹ ɛ p]
Occurrences:
32,534
Examples:
* inked:
[ɪ ŋ k t]
* likes:
[l aj k s]
* hoch:
[h ɑ k]
* fixes:
[ ɪ k s ɪ z]
Occurrences:
17,059
Examples:
* digha:
[d aj ɡ ɑː]
* hang:
[h a ŋ ɡ]
* glued:
[ɡ l ɾ]
* groom:
[ɡ ɹ ʉː m]
Occurrences:
435
Examples:
* that:
[ð a ʔ]
* jet:
[ ɛ ʔ]
* sight:
[s aj ʔ]
* rate:
[ɹ ej ʔ]

Affricate

Occurrences:
7,118
Examples:
* macho:
[m ɑ ow]
* chu:
[ u]
* chalk:
[ ɔ k]
* chevy:
[ ɛ i]
Occurrences:
11,759
Examples:
* drank:
[ æ ŋ k]
* bejan:
[ ə n]
* geo:
[ i ow]
* joann:
[ əw a n]

Sibilant

Occurrences:
81,177
Examples:
* clits:
[c ʎ ɪ t s]
* afc:
[ej ɛ f s ]
* bacs:
[b æ k s]
* syria:
[s ɪ ɹ i ə]
Occurrences:
33,048
Examples:
* lodz:
[l ɒ d z]
* rinds:
[ɹ aj n d z]
* cise:
[s aj z]
* starz:
[s t ɑː z]
Occurrences:
16,175
Examples:
* mesh:
[m ɛ ʃ]
* plush:
[p l ɐ ʃ]
* ashen:
[æ ʃ ɪ n]
* shod:
[ʃ ɑ d]
Occurrences:
893
Examples:
* azusa:
[a ʒ ʉː z ə]
* uzhe:
[j ʉː ʒ]
* azure:
[a ʒ ɒː]
* niger:
[ɲ ʒ ɛ ɹ]

Fricative

Occurrences:
18,520
Examples:
* rfk:
[ɑ ɹ ɛ f ej]
* lief:
[ʎ f]
* stfan:
[s t ɛ f æ n]
* ruft:
[ɹ ɔ f t]
Occurrences:
5,062
Examples:
* flick:
[ ʎ ɪ k]
* feu:
[ ʉː]
* fink:
[ i ŋ k]
* filly:
[ ɪ ʎ i]
Occurrences:
3
Examples:
* fwoom:
[ ʉː m]
Occurrences:
13,831
Examples:
* valle:
[v a ʎ ]
* clive:
[k l aj v]
* wave:
[w e v]
* var:
[v ɑ ɹ]
Occurrences:
3,112
Examples:
* vela:
[ l ə]
* vena:
[ n ə]
* heavy:
[h ɛ i]
* livid:
[ʎ ɪ ɪ d]
Occurrences:
8
Examples:
* vworp:
[ ɒː p]
Occurrences:
5,857
Examples:
* thay:
[θ ej]
* berth:
[b θ]
* maths:
[m æ θ s]
* oaths:
[əw θ s]
Occurrences:
1,368
Examples:
* oaths:
[əw ð z]
* thy:
[ð aj]
* their:
[ð ɛ ɹ]
* thou:
[ð əw]
Occurrences:
2,158
Examples:
* hinny:
[ç i ɲ i]
* waihi:
[w aj ç ]
* gyro:
[ç ɪ ɹ ow]
* hick:
[ç i k]
Occurrences:
8,540
Examples:
* hmm:
[h ə m]
* nihoa:
[ɲ h əw ə]
* whom:
[h m]
* hoyle:
[h ɔj ɫ]

Approximant

Occurrences:
9,197
Examples:
* blois:
[b ɫ w a]
* wined:
[w aj n d]
* wonka:
[w ɐ ŋ k ə]
* whos:
[w ʉː z]
Occurrences:
69,939
Examples:
* paria:
[ ə ɹ aj ə]
* bargo:
[b ɑ ɹ ɡ ow]
* forma:
[f ɒ ɹ m ə]
* braai:
[b ɹ aj]
Occurrences:
3,280
Examples:
* utica:
[j ʉː ɪ k ə]
* yee:
[j i]
* yer:
[j ɝ]
* abaya:
[ə b ej j ə]

Tap

Occurrences:
478
Examples:
* read:
[ɹ ɛ ɾ]
* code:
[ əw ɾ]
* honor:
[ɒ ɾ̃ ə]
* lord:
[l ɒː ɾ]
Occurrences:
70
Examples:
* ready:
[ɹ ɛ ɾʲ i]
* idea:
[aj ɾʲ j ə]
* added:
[a ɾʲ ɪ ɾ]
* body:
[b ɒ ɾʲ i]
Occurrences:
67
Examples:

Lateral

Occurrences:
29,417
Examples:
* slur:
[s l ɝ]
* plays:
[p l e z]
* claus:
[k l ɒː s]
* malar:
[m ej l ə]
Occurrences:
24,653
Examples:
* ideal:
[aj a ɫ]
* tile:
[ aj ɫ]
* jell:
[ ɛ ɫ]
* mohel:
[m əw ɛ ɫ]
Occurrences:
58
Examples:
Occurrences:
20,831
Examples:
* listi:
[ʎ i s i]
* oglio:
[əw ʎ əw]
* paley:
[ ej ʎ i]
* linc:
[ʎ ɪ ŋ k]

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:
63,712
Examples:
* petri:
[ ɛ ɹ i]
* wiley:
[w aj ʎ i]
* cali:
[ æ ʎ i]
* beef:
[ i f]
Occurrences:
17,090
Examples:
* slc:
[ɛ s ɛ ɫ s ]
* ipcc:
[aj s s ]
* chico:
[ʃ k ow]
* gci:
[ s aj]
Occurrences:
2,829
Examples:
* nehru:
[ɲ ej ɹ ʉ]
* sotho:
[s ʉ ʉ]
* fluke:
[f l ʉ k]
* mutsu:
[m ʊ t s ʉ]
Occurrences:
8,244
Examples:
* educe:
[ɪ ʉː s]
* loom:
[l ʉː m]
* supra:
[s ʉː p ɹ ə]
* ploom:
[p l ʉː m]
Occurrences:
4,034
Examples:
* lujvo:
[l u ʃ v o]
* apus:
[e p u s]
* caput:
[ a p u t]
* sinus:
[s aj n u s]
Occurrences:
4,665
Examples:
* taboo:
[ a b ]
* neuk:
[ɲ k]
* tuam:
[ a m]
* vertu:
[v ]
Occurrences:
72,843
Examples:
* blink:
[b ʎ ɪ ŋ k]
* akin:
[ɐ c ɪ n]
* silt:
[s ɪ ɫ t]
* grim:
[ɟ ɹ ɪ m]
Occurrences:
8,061
Examples:
* uttar:
[ʊ t ɚ]
* tunde:
[ ʊ n d e]
* lure:
[ʎ ʊ ə]
* mure:
[m ʊ ɹ]

Close-Mid

Occurrences:
7,660
Examples:
* rave:
[ɹ e v]
* acog:
[e a ɡ]
* phase:
[f e z]
* meiji:
[m e i]
Occurrences:
17,268
Examples:
* bae:
[b ej]
* naomi:
[ɲ ej ə i]
* copay:
[ ow ej]
* mayor:
[m ej ɹ]
Occurrences:
7,002
Examples:
* soc:
[s o ʃ]
* yobbo:
[j ɔ b o]
* owens:
[o w ɛ n z]
* bone:
[b o n]
Occurrences:
10,089
Examples:
* boden:
[b ow d ə n]
* sonia:
[s ow ɲ i ə]
* throw:
[θ ɹ ow]
* tote:
[ ow t]
Occurrences:
86,311
Examples:
* ryan:
[ɹ aj ə n]
* berat:
[b ə ɹ a t]
* ongar:
[ɒ ŋ ɡ ə]
* haifa:
[ç ej f ə]
Occurrences:
9,815
Examples:
* liter:
[ʎ t ɚ]
* tuner:
[ ʉː n ɚ]
* your:
[j ɚ]
* enter:
[ɛ n t ɚ]

Open-Mid

Occurrences:
46,399
Examples:
* fret:
[f ɹ ɛ t]
* tens:
[ ɛ n z]
* yet's:
[j ɛ t s]
* nello:
[n ɛ l ow]
Occurrences:
1,565
Examples:
* bear:
[b ɛː]
* khmer:
[ ə m ɛː]
* aires:
[ɛː z]
* aire:
[ɛː]
Occurrences:
308
Examples:
* murr:
[m ɜ]
* furor:
[ ɜ ɹ ə]
* circe:
[s ɜ s i]
* turku:
[ ɜ k ʉː]
Occurrences:
3,191
Examples:
* pasir:
[ ə s ɜː]
* yrneh:
[ɜː ɲ i]
* wurtz:
[w ɜː t s]
* wurst:
[v ɜː s t]
Occurrences:
3,490
Examples:
* viera:
[ ɝ ə]
* vert:
[v ɝ t]
* fern:
[f ɝ n]
* rural:
[ɹ ɝ ə ɫ]
Occurrences:
23,458
Examples:
* tubby:
[ ɔ i]
* maud:
[m ɔ d]
* pork:
[ ɔ k]
* agora:
[a ɡ ɔ ɹ a]
Occurrences:
15,904
Examples:
* slams:
[s l æ m z]
* graf:
[ɡ ɹ æ f]
* patty:
[ æ i]
* acids:
[æ s ɪ d z]
Occurrences:
10,496
Examples:
* sudds:
[s ɐ d z]
* rutch:
[ɹ ɐ ]
* zuppa:
[z ɐ p ə]
* brush:
[b ɹ ɐ ʃ]

Open

Occurrences:
47,485
Examples:
* arc:
[a k]
* caron:
[ a ə n]
* mega:
[m ɛ ɡ a]
* fanon:
[f a n u n]
Occurrences:
2,143
Examples:
* yrneh:
[ ɲ i]
* overt:
[o v t]
* earl:
[ ɫ]
* adieu:
[a ]
Occurrences:
11,027
Examples:
* tonia:
[ ɑ ɲ i ə]
* zohar:
[z ow h ɑ ɹ]
* dobbs:
[d ɑ b z]
* jot:
[ ɑ t]
Occurrences:
7,178
Examples:
* short:
[ʃ ɑː t]
* horse:
[h ɑː s]
* ala:
[ɑː l ə]
* anput:
[ɑː n ʊ t]
Occurrences:
14,872
Examples:
* goth:
[ɡ ɒ θ]
* ogle:
[ɒ ɡ ə ɫ]
* frock:
[f ɹ ɒ k]
* cores:
[ ɒ ɹ z]
Occurrences:
5,776
Examples:
* yaws:
[j ɒː z]
* audi:
[ɒː i]
* utahn:
[j ʉː ɒː n]
* porto:
[ ɒː əw]

Diphthongs#

  • aj

  • aw

  • ej

  • ow

  • ɔj

  • əw