SlowGuess/ACL_2020_OCR huggingface dataset:
Ting Qian, Kristy Hollingshead, Su-youn Yoon, Kyoung-young Kim, and Richard Sproat. 2010. A Python toolkit for universal transliteration. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Karim Rahim and Wesley S. Burr. 2017. multitaper: Multitaper spectral analysis. R package version 1.0-14.
Daniel Recasens and Aina Espinosa. 2009. Dispersion and variability in Catalan five and six peripheral vowel systems. Speech Communication, 51(3):240-258.
Tanja Schultz. 2002. GlobalPhone: A multilingual speech and text database developed at Karlsruhe University. In Seventh International Conference on Spoken Language Processing, pages 345-348, Denver, CO.
Jean-Luc Schwartz and Lucie Ménard. 2019. Structured idiosyncrasies in vowel systems. OSF Preprints.
Christine H. Shadle, Wei-rong Chen, and D. H. Whalen. 2016. Stability of the main resonance frequency of fricatives despite changes in the first spectral moment. The Journal of the Acoustical Society of America, 140(4):3219-3220.
Kenneth N. Stevens and Samuel J. Keyser. 2010. Quantal theory, enhancement and overlap. Journal of Phonetics, 38(1):10-19.
Andreas Stolcke. 2002. SRILM - an extensible language modeling toolkit. In Seventh International Conference on Spoken Language Processing, pages 901-904.
Bert Vaux and Bridget Samuels. 2015. Explaining vowel systems: Dispersion theory vs natural selection. Linguistic Review, 32(3):573-599.
Dominic J. L. Watt. 2000. Phonetic parallels between the close-mid vowels of Tyneside English: Are they internally or externally motivated? Language Variation and Change, 12(1):69-101.
John C. Wells. 1995/2000. Computer-coding the IPA: A proposed extension of SAMPA.
D.H. Whalen and Andrea G. Levitt. 1995. The universality of intrinsic F0 of vowels. Journal of Phonetics, 23:349-366.
Matthew Wiesner, Oliver Adams, David Yarowsky, Jan Trmal, and Sanjeev Khudanpur. 2019. Zero-shot pronunciation lexicons for cross-language acoustic model transfer. In Proceedings of IEEE Association for Automatic Speech Recognition and Understanding (ASRU).
Xiaohui Zhang, Vimal Manohar, Daniel Povey, and Sanjeev Khudanpur. 2017. Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework. arXiv preprint arXiv:1706.03747.
Eberhard Zwicker and Ernst Terhardt. 1980. Analytical expressions for critical-band rate and critical bandwidth as a function of frequency. The Journal of the Acoustical Society of America, 68(5):1523-1525.
A Pairwise Correlations between Vowel Formant Measures (§4 Case Studies)
Table 3 and Table 4 respectively show Pearson correlations of mean F1 and mean F2 in ERB between vowels that appear in at least 10 readings. As formalized in the present analysis, phonetic uniformity predicts strong correlations of mean F1 among vowels with a shared height specification, and strong correlations of mean F2 among vowels with a shared backness specification. The respective “Height” and “Backness” columns in Table 3 and Table 4 indicate whether the vowels in each pair match in their respective specifications.  $p$ -values are corrected for multiple comparisons using the Benjamini-Hochberg correction and a false discovery rate of 0.25 (Benjamini and Hochberg, 1995). Significance is assessed at  $\alpha = 0.05$  following the correction for multiple comparisons; rows that appear in gray have correlations that are not significant according to this threshold.
V1	V2	Height	# Readings	r	p
/i/	/i:/	✓	12	0.81	0.006
/e:/	/o:/	✓	10	0.81	0.015
/i/	/u/	✓	40	0.79	0.000
/ε/	/ɔ/	✓	11	0.68	0.053
/o/	/a/	37	0.66	0.000
/i:/	/o:/	11	0.65	0.070
/i:/	/u:/	✓	12	0.64	0.061
/e/	/o/	✓	35	0.62	0.001
/e/	/u/	36	0.59	0.001
/e/	/a/	34	0.58	0.002
/u/	/ə/	12	0.58	0.105
/i:/	/e:/	11	0.58	0.118
/i/	/e/	38	0.54	0.002
/ε/	/a/	12	0.54	0.127
/u/	/o/	38	0.49	0.007
/ε/	/u/	14	0.49	0.135
/i/	/o/	39	0.46	0.011
/e/	/ε/	✓	12	0.46	0.204
/u/	/a/	37	0.42	0.027
/i:/	/e/	11	0.42	0.288
/u/	/u:/	✓	10	0.41	0.334
/i:/	/u/	✓	11	0.33	0.430
/i:/	/a/	11	0.28	0.496
/i/	/a/	39	0.27	0.173
/i/	/ε/	14	0.24	0.496
/i:/	/o/	13	0.19	0.624
/i/	/ə/	13	0.10	0.785
/u/	/ɔ/	12	0.09	0.785
/ε/	/o/	✓	13	-0.09	0.785
/e/	/ɔ/	✓	10	-0.12	0.785
/u:/	/o/	10	-0.12	0.785
/i/	/ɔ/	11	-0.42	0.288
/o/	/ə/	✓	11	-0.51	0.173
/ə/	/a/	11	-0.90	0.001
Table 3: Pearson correlations  $(r)$  of mean F1 in ERB between vowel categories.
V1	V2	Backness	# Readings	r	p
/e/	/ε/	✓	12	0.77	0.019
/u/	/u:/	✓	10	0.77	0.037
/i/	/i:/	✓	12	0.70	0.038
/u/	/o/	✓	38	0.69	0.000
/i/	/ε/	✓	14	0.69	0.031
/u:/	/o/	✓	10	0.62	0.130
/u/	/ə/	12	0.60	0.107
/u/	/ɔ/	✓	12	0.52	0.168
/i/	/e/	✓	38	0.41	0.038
/ε/	/a/	12	0.32	0.519
/o/	/a/	37	0.30	0.159
/e:/	/o:/	10	0.27	0.666
/e/	/a/	34	0.24	0.339
/o/	/ə/	11	0.21	0.724
/ə/	/a/	✓	11	0.16	0.830
/i:/	/e/	✓	11	0.11	0.911
/i/	/a/	39	0.06	0.911
/i:/	/e:/	✓	11	0.06	0.965
/e/	/o/	35	0.01	0.965
/u/	/a/	37	0.00	0.985
/ε/	/ɔ/	11	-0.03	0.965
/i:/	/a/	11	-0.04	0.965
/ε/	/o/	13	-0.04	0.965
/e/	/u/	36	-0.12	0.666
/ε/	/u/	14	-0.22	0.666
/i/	/ə/	13	-0.23	0.666
/i:/	/o:/	11	-0.42	0.345
/i/	/o/	39	-0.48	0.017
/i:/	/o/	13	-0.52	0.149
/i/	/u/	40	-0.55	0.003
/i/	/ɔ/	11	-0.63	0.107
/e/	/ɔ/	10	-0.65	0.107
/i:/	/u/	11	-0.80	0.019
/i:/	/u:/	12	-0.83	0.009
Table 4: Pearson correlations  $(r)$  of mean F2 in ERB between vowel categories.
B Distributions of Unitran Segment Accuracy (§3.1.3 Quality Measures)
Here we evaluate the quality of the Unitran dataset in more detail. The goal is to explore the variation in the quality of the labeled Unitran segments across different languages and phoneme labels. This evaluation includes only readings in high-resource languages, where we have not only the aligned Unitran pronunciations but also aligned high-resource pronunciations (Epitran or WikiPron) against which to evaluate them. The per-token statistics used to calculate these plots are included in the corpus release to enable closer investigation of individual phonemes than is possible here.
B.1 Unitran Pronunciation Accuracy
First, in Figures 5 and 6, we consider whether Unitran's utterance pronunciations are accurate without looking at the audio. For each utterance, we compute the unweighted Levenshtein alignment between the Unitran pronunciation of the utterance and the high-resource pronunciation. For each reading, we then score the percentage of Unitran 'phoneme' tokens that were aligned to high-resource 'phoneme' tokens with exactly the same label.[14] We can see in Figure 6 that many labels are highly accurate in many readings while being highly inaccurate in many others. Some labels are noisy in some readings.[15]
Figure 5: Unitran pronunciation accuracy per language, evaluated by Levenshtein alignment to WikiPron pronunciations (hatched bars) or Epitran pronunciations (plain bars). Where a language has multiple readings, error bars show the min and max across those readings.
Figure 6: Unitran pronunciation accuracy per language, for selected phonemes. Accuracy is evaluated by Levenshtein alignment as in Figure 5. Each curve is a kernel density plot with integral 1. For the  $/z/$  curve, the integral between  $80\%$  and  $100\%$  (for example) is the estimated probability that in a high-resource language drawn uniformly at random, the fraction of Unitran  $/z/$  segments that align to high-resource  $/z/$  segments falls in that range. The 'all' curve is the same, but now the uniform draw is from all pairs of (high-resource language, Unitran phoneme used in that language).
<sup>14</sup>By contrast, PER in Table 1 aligns at the word level rather than the utterance level, uses the number of symmetric alignment errors (insertions + deletions + substitutions) rather than the number of correct Unitran phonemes, and normalizes by the length of the high-resource ‘reference’ pronunciation rather than by the length of the Unitran pronunciation.
15Note that as §3.1.3 points out, it may be unfair to require exact match of labels, since annotation schemes vary.)
B.2 Unitran Segment Label Accuracy
In Figures 7 and 8, we ask the same question again, but making use of the audio data. The match for each Unitran segment is now found not by Levenshtein alignment, but more usefully by choosing the high-resource segment with the closest midpoint. For each reading, we again score the percentage of Unitran 'phoneme' tokens whose aligned high-resource 'phoneme' tokens have exactly the same label. Notice that phonemes that typically had high accuracy in Figure 6, such as /p/ and /b/, now have far more variable accuracy in Figure 8, suggesting difficulty in aligning the Unitran pronunciations to the correct parts of the audio.
Figure 7: Unitran pronunciation accuracy per language, as in Figure 5 but with audio midpoint alignment in place of Levenshtein alignment.
Figure 8: Unitran pronunciation accuracy per language, for selected phonemes, as in Figure 6 but with audio midpoint alignment in place of Levenshtein alignment.
B.3 Unitran Segment Boundary Accuracy
Finally, in Figures 9 and 10, we measure whether Unitran segments with the "correct" label also have the "correct" time boundaries, where "correctness" is evaluated against the corresponding segments obtained using Epitran or WikiPron+G2P.
Figure 9: Mean error per language in the temporal boundaries of Unitran segments.. Each Unitran segment is evaluated against the WikiPron segment (hatched bars) or Epitran segment (plain bars) with the closest midpoint, as if the latter were truth. The error of a segment is the absolute offset of the left boundary plus the absolute offset of the right boundary. Only segments where the Unitran label matches the Epitran/WikiPron label are included in the average. Where a language has multiple readings, error bars show the min and max across those readings.
Figure 10: Mean error per language in the temporal boundaries of Unitran segments, for selected phonemes. Each curve is a kernel density plot with integral 1. For the /z/ curve, the integral between 50ms and 100ms (for example) is the estimated probability that in a high-resource language drawn uniformly at random, the Unitran /z/ segments whose corresponding Epitran or WikiPron segments are also labeled with /z/ have mean boundary error in that range. Small bumps toward the right correspond to individual languages where the mean error of /z/ is unusually high. The 'all' curve is the same, but now the uniform draw is from all pairs of (high-resource language, Unitran phoneme used in that language). The boundary error of a segment is evaluated as in Figure 9.
C WikiPron Grapheme-to-Phoneme (G2P) Accuracy (§3.1.3 Quality Measures)
For each language where we used WikiPron, Table 5 shows the phoneme error rate (PER) of Phonetisaurus G2P models trained on WikiPron entries, as evaluated on held-out WikiPron entries. This is an estimate of how accurate our G2P-predicted pronunciations are on out-of-vocabulary words, insofar as those are distributed similarly to the in-vocabulary words. (It is possible, however, that out-of-vocabulary words such as Biblical names are systematically easier or harder for the G2P system to pronounce, depending on how they were transliterated.)
The same G2P configuration was used for all languages, with the hyperparameter settings shown in Table 6. (seq1_max and seq2_max describe how many tokens in the grapheme and phoneme sequences can align to each other.). These settings were tuned on SIGMORPHON 2020 Task 1 French, Hungarian, and Korean data (Gorman et al., 2020), using 20 random 80/20 splits.
ISO 639-3	fin	lat	nhx	srn	mah	por-po	mfe	mww	por-bz	eng	khm	mlg	ori	ban	urd
Train size	41741	34181	126	157	813	9633	203	227	10077	54300	3016	114	211	172	704
PER	0.8	2.4	4.1	4.6	9.6	10.1	10.7	10.8	11.4	14.5	15.5	15.8	16.1	19.5	26.7
±0.02	±0.04	±1.02	±0.76	±0.41	±0.11	±1.2	±1.29	±0.16	±0.06	±0.38	±1.44	±1.13	±1.35	±0.60
Table 5: WikiPron G2P Phone Error Rate (PER) calculated treating WikiPron annotations as ground-truth. We perform 20 trials with random 80/20 splits per language, and report PER averaged across trials with  $95\%$  confidence intervals for each language.
Phonetisaurus Alignment Hyperparameters	seq1_max 1	seq2_max 3	seq1_del True	seq2_del True	grow True	max EM iterations 11
Graphone Language Model Hyperparameters	n-gram order 5	LM type max-ent	discounting Kneser-Ney	gt2min 2	gt3min 2	gt4min 3	gt5min 4
Table 6: Table of final G2P hyperparameter settings. Alignment parameters not listed here for phonetisaurus-align use the default values. The language model was trained using SRILM (Stolcke, 2002) ngram-count using default values except for those listed above.
D Retention Statistics (§4.1 Data Filtering)
Table 7 shows what percentage of tokens would be retained after various methods are applied to filter out questionable tokens from the readings used in §4.1. In particular, the rightmost column shows the filtering that was actually used in §4.1. We compute statistics for each reading separately; in each column we report the minimum, median, mean, and maximum statistics over the readings. The top half of the table considers vowel tokens (for the vowels in Appendix A); the bottom half considers sibilant tokens (/s/ and /z/).
On the left side of the table, we consider three filtering techniques for Unitran alignments. Midpoint retains only the segments whose labels are "correct" according to the midpoint-matching methods of Appendix B. MCD retains only those utterances with  $\mathrm{MCD} < 6$ . Outlier removes tokens that are outliers according to the criteria described in §4.1. Finally, AGG. is the aggregate retention rate retention rate after all three methods are applied in order.
On the right side of the table, we consider the same filtering techniques for the high-resource alignments that we actually use, with the exception of Midpoint, as here we have no higher-quality annotation to match against.
Unitran Alignments	High-Resource Alignments
# Tokens	Midpoint	MCD	Outlier	AGG.	# Tokens	MCD	Outlier	AGG.
Vowels	Min	50,132	2%	42%	83%	1%	61,727	42%	84%	37%
Median	21,5162	23%	88%	90%	16%	232,059	88%	90%	79%
Mean	23,9563	25%	81%	89%	20%	223,815	81%	90%	73%
Max	662,813	65%	100%	93%	60%	468,864	100%	93%	93%
# Readings	49	46	48	49	45	49	48	49	48
Sibilants	Min	7,198	10%	42%	89%	13%	7184	44%	91%	43%
Median	28,690	70%	87%	97%	59%	27569	87%	97%	85%
Mean	30,025	63%	80%	95%	56%	27083	81%	96%	79%
Max	63,573	89%	100%	98%	79%	45,290	100%	99%	96%
# Readings	36	26	35	36	19	25	22	25	22
Table 7: Summary of quality measure retention statistics for vowels and sibilants over unique readings with reading-level MCD  $< 8$  for Unitran and high-resource alignments.
E All VoxClamantis v1.0 Languages
All 635 languages from 690 readings are presented here with their language family, ISO 639-3 code, and mean utterance alignment quality in Mel Cepstral Distortion (MCD) from Black (2019). Languages for which we release Epitran and/or WikiPron alignments in addition to Unitran alignments are marked with  $e$  and  $w$  respectively. MCD ranges from purple (low), blue-green (mid), to yellow (high). Lower MCD typically corresponds to better audio-text utterance alignments and higher quality speech synthesis, but judgments regarding distinctions between languages may be subjective. ISO 639-3 is not intended to provide identifiers for dialects or other sub-language variations, which may be present here where there are multiple readings for one ISO 639-3 code. We report the most up-to-date language names from the ISO 639-3 schema (Eberhard and Fennig, 2020). Language names and codes in many schema could be pejorative and outdated, but where language codes cannot be easily updated, language names can and often are.
NIGER-CONGO: 159
Abidji abi	6.3
Adele ade	6.9
Adioukrou adj	7.4
Akan aka	7.8
Akebu keu	7.0
Akoose bss	7.2
Anufo cko	6.9
Avatime avn	6.3
Bafut bfd	7.3
Bandial bqj	7.0
Bekwarra bkv	7.3
Bete-Bendi btt	9.1
Biali beh	7.6
Bimoba bim	7.0
Bokobaru bus	6.9
Bomu bmq	7.0
Buamu box	8.1
Buli (Ghana) bwu	7.3
Bum bmv	6.4
Cameroon Mambila mcu	7.6
Central-Eastern Niger fuq	7.1
Cerma cme	8.5
Cerma cme	6.1
Chopi cce	6.3
Chumburung ncu	7.3
Delo ntr	8.0
Denya anv	6.7
Ditammari tbz	7.7
Djimini Senoufo dyi	7.1
Duruma dug	6.7
Eastern Karaboro xrb	8.1
Ekajuk eka	7.5
Ewe ewe	6.3
Ewe ewe	6.7
Farefare gur	8.1
Farefare gur	8.3
Fon fon	8.7
Gikyode acd	7.7
Giryama nyf	6.8
Gitonga toh	6.8
Gogo gog	7.0
Gokana gkn	8.0
Gourmanchégaux	7.3
Gwere gwv	6.1
Hanga hag	7.2
Haya hay	7.1
Ifé ife	7.8
Ivbie North-Okpela-Ar atg	7.7
Izere izr	6.8
Jola-Fonyi dyo	7.1
Jola-Kasa esk	7.5
Jukun Takum jhu	7.9
Kabiyè kbp	7.4
Kagulu kki	6.6
Kako kkj	7.9
Kasem xsm	7.7
Kasem xsm	8.0
Kenyang ken	7.4
Kim kia	6.8
Kim kia	6.3
Koma kmy	7.3
Konkombaxon	7.8
Kono (Sierra Leone) kno	8.1
Koonzime ozm	8.0
Kouya kyf	8.2
Kukele kez	7.8
Kunda kdn	6.4
Kuo xuo	6.7
Kusaal kus	7.0
Kutep kub	6.9
Kutu kdc	5.7
Kuwataay cwt	7.4
Kwere cwe	7.5
Lama (Togo) las	7.9
Lelemi lef	7.3
Lobi lob	7.0
Lokaa yaz	6.6
Lukpa dop	8.0
Lyélé lee	8.1
Machame jmc	6.8
Mada (Nigeria) mda	6.6
Makaa mcp	6.9
Makhwuva vmw	6.8
Malawi Lomwe lon	5.8
Malba Birifor bfo	6.5
Mamara Senoufo myk	8.0
Mampruli maw	7.6
Mankanya knf	6.6
Masaaba myx	6.1
Meta'mgo	6.4
Miyobe soy	7.2
Moba mtq	8.1
Moba mtq	7.2
Mochi old	6.9
Mossi mos	7.2
Mossi mos	7.5
Mumuye zmm	7.7
Mundani mnf	6.8
Mwan moa	7.8
Mwani wmv	6.5
Mündü muh	8.4
Nafaanra nfr	6.8
Nande nb	7.2
Nateni ntm	7.4
Nawdm nzm	8.3
Ndogo ndz	6.9
Ngangam gng	8.0
Nigeria Mambila mzK	6.9
Nilamba nim	6.7
Ninzo nin	5.9
Nkonya nko	6.8
Noone nhu	7.2
Northern Dagara dgi	7.3
Ntcham bud	8.8
Nyabwa nwB	7.7
Nyakyusa-Ngonde nyy	6.7
Nyankole nyn	8.0
Nyaturu rim	6.7
Nyole nuj	5.9
Nyoro nyo	7.1
Nzima nzi	7.2
Obolo ann	8.5
Oku oku	8.3
Pasaal sig	7.5
Plapo Krumen ktj	7.0
Pokomo pkb	6.5
Pular fuf	7.6
Rigwe iri	7.3
Rundi run	8.3
Saamia lsm	6.8
Sango sag	6.7
Sekpele lip	6.6
Selee snw	6.5
Sena seh	6.6
Shambala ksb	6.4
Sissala sld	7.6
Siwu akp	6.3
Soga xog	6.9
South Fali fal	7.7
Southern Birifor biv	7.6
Southern Bobo Madaré bwc	7.6
Southern Dagaare dga	6.5
Southern Nuni nww	7.6
Southwest Gbaya gso	7.6
Supyire Senoufo spp	8.3
Talinga-Bwisi tij	6.5
Tampulma tpm	7.1
Tharaka thk	7.8
Tikar tik	7.8
Timne tem	7.2
Toura (Côte d'Ivoire) neb	6.8
Tsonga tso	5.2
Tumulung Sisaala sil	8.0
Tuwuli bov	6.2
Tyap kcg	7.5
Vengo bay	6.7
Vunjo vun	6.5
West-Central Limba lia	7.3
Yocoboué Dida gud	7.0
AUSTRONESIAN: 106
Achinese ace	6.5
Agutaynen agn	6.1
Alangan alj	5.9
Alune alp	6.3
Ambai amk	5.4
Amganad Ifugao ifa	5.9
Aralle-Tabulahan atq	6.7
Arop-Lokep apr	6.2
Arosi aia	5.6
Bada (Indonesia) bhz	5.4
Balantak blz	6.1
Balinese ban	6.4
Bambam ptu	5.8
Batad Ifugao ifb	6.2
Batak Dairi btd	6.1
Batak Karo btx	6.2
Batak Simalungun bts	6.4
Besoap beq	6.4
Brooke's Point Palawa plw	6.2
Caribbean Javanese jyn	6.8
Cebuano ceb	6.9
Central Bikol bel	6.5
Central Malay pse	6.6
Central Mnong cmo	6.0
Central Sama sml	6.7
Da'a Kaili kzf	6.5
Duri mvp	6.9
Fataleka far	6.3
Fijian fij	7.6
Fordata frd	5.3
Gilbertese gil	7.0
Gorontalo gor	6.2
Hanunoo hnn	6.0
Hiligaynon hil	6.7
Iban iba	6.5
e Iloko ilo	6.5
e Indonesian ind	7.2
e Indonesian ind	6.8
e Indonesian ind	6.4
Itawit itv	6.6
e Javanese jav	6.3
Kadazan Dusun dtp	8.5
Kagayanen cgc	6.2
Kalagan kqe	5.9
Kankanaey kne	5.7
Keley-I Kallahan ify	6.2
Khehek tlx	9.1
Kilivila kij	6.2
Kinaray-A krj	6.3
Kisar kje	6.3
Koronadal Blaan bpr	6.4
Lampung Api ljp	6.4
Lauje law	6.4
Ledo Kaili lew	7.0
Luang lex	6.1
Lundayeh lnd	6.5
Ma'anyan mhy	6.4
Madurese mad	7.4
Mag-antsi Aytasgb	6.4
Makasar mak	6.4
Malagasy mlg	8.8
Malagasy mlg	7.3
Malagasy mlg	6.3
Malay (macrolanguage) msa	6.3
e Malay (macrolanguage) msa	6.0
Mamasam qmj	6.3
Manado Malay xmm	5.2
Mapos Buang bzh	5.8
Marano mrw	6.0
Marshallese mah	7.9
Matigsalug Manobo mbt	6.4
Mayoyao Ifugao ifu	6.6
Mentawai mww	6.6
Minangkabau min	6.3
Misima-Panaeati mpx	6.3
Mongondow mog	6.7
Muna mnb	6.3
Napun py	6.7
Ngaju nij	7.3
Nias nia	6.8
Obo Manobo obo	5.8
Owa stn	6.3
Palauan pau	6.7
Pamona pmf	6.3
Pampanga pam	6.6
Pangasinan pag	6.7
Paranan prf	6.2
Rejiang rej	6.0
Roviana rug	5.7
Sambal xsb	6.0
Sambal xsb	6.0
Samoan smo	6.0
Sangir sxn	7.7
Sarangani Blaan bps	6.5
Sasak sas	6.3
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