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Detecting abbreviations is crucial for proper text normalization and subsequent pronunciation generation. In broad terms, “abbreviation” means shortening, contraction, initialism, or acronym. In this post, we will focus on initialisms - entities made up of initial letters of words, which are pronounced as separate letters. It is essential to detect those reliably in TTS Frontend because custom pronunciation generation is needed. Additionally, proper detection of initialisms reduces the ambiguity of identifying sentence boundaries.

Usually, initialisms are written in capital letters, where each letter is followed by a dot, for example “F.B.I.” It is also common to write initialism without dots, i.e., “AI”, “USA” or “IBM”. Unfortunately, sometimes initialisms are written in lower case: “hr” or “www”.

As a result following disambiguation problems are typical:

  • Confusing with single-letter words. Simple case since the set of words is minimal (“I” and “a”).
  • Confusing with roman numerals. Even though roman numerals are somewhat rare, they are written as a sequence of capital letters from a certain set, which can be easily confusing.
  • Confusing with capitalized words. The most frequent case is when a capitalized word is recognized as initialism while it is just editing specifics.
  • Confusing with acronyms. Acronyms follow the same patterns as initialisms, but they are not spelled out. For ex. “NATO” or “AIDS”
  • Low-case abbreviations. Rather rare situation, when abbreviation is not capitalized and marked.

The typical solution for the overall problem of abbreviations is summarized in [1]:

A solid solution to the problem of upper-case tokens is to assume that in a well-developed system all genuine acronyms will appear in the lexicon. Upon encountering a capitalised token, we first check whether it is in the lexicon and, if so, just treat it as a normal word. If not, we then split the token into a series of single-character tokens and designate each as a letter. There is a possible alternative, where we examine upper-case tokens and attempt to classify them as either acronyms or letter sequences. Such a classification would be based on some notion of “pronounceability” such that if the token contains vowel characters in certain places then this might lead us to think that it is an acronym instead of a letter sequence. Experience has shown that this approach seldom works (at least in English) since there are plenty of potentially pronounceable tokens (e.g. USA and IRA) that sound absurd if we treat them as acronyms (e.g. /y uw s ax/ and /ay r ax/). If a mistake is to be made, it is generally better to pronounce an acronym as a letter sequence rather than the other way round.

Simple cases

To narrow down the task, let’s first outline use cases that should be spelled letter by letter without any ambiguity.

[A-Za-z] character with the dot after it

This applies to both lower- and upper-cases (both “f.b.i.” and “F.B.I.” are possible). We also assume that a single character with the dot after it should be spelled out too. It might be incorrect if the article “a” or pronoun “I” is at the end of a sentence. But those cases are grammatically incorrect, so let’s assume those cases are initialisms, shortened first names, or list indices, i.e., they should be spelled out. Another potential issue is a single roman digit at the end of the sentence. For those cases, let’s assume it’s an abbreviation unless it’s not in the list of exceptions for roman numbers, such as “Clement I.” This rule can be expressed by simple acceptor [2]:

delete_dot = pynutil.delete(".")
dot_abbr = pynini.closure((UPPER | (LOWER @ TO_UPPER)) + delete_dot, 1)


When a word consists of only consonants (both upper- and lower-case), there is no way to read it other than spelling. The only gotcha is the letter “y,” which can act like a vowel, for example, “by.” Acceptor looks like this:

CONSONANTS = 'bcdfghjklmnpqrstvwxz'

def _any_element_lower(element_lst_):
    return pynini.union(*[pynini.accep(x.lower()) @ pynini.closure(TO_UPPER) for x in element_lst_])

def _any_element_upper(element_lst_):
    return pynini.union(*[pynini.accep(x.upper()) for x in element_lst_])

def _any_element(element_lst_):
    return _any_element_lower(element_lst_) | _any_element_upper(element_lst_)

consonant_abbr = pynini.closure(_any_element(CONSONANTS), 1)

vowels only

When a word contains only vowels, it should also be spelled out. However, single-letter is likely not an abbreviation (consider “I” or “U”). For upper-case, it makes sense to treat any sequence of vowels starting from length 2 as an abbreviation (for ex. “AI” or “IEEE”). For lower-case, there are two-letter vowel-only words (for ex., interjection “oi”). So to be safe, a word should be considered an abbreviation only if it contains 3 lower-case vowels and more.

VOWELS = 'aeiou'

vowel_abbr = pynini.closure(_any_element_upper(VOWELS), 2) |
             pynini.closure(_any_element_lower(VOWELS), 3)


As specified in the quote from [1], for non-obvious cases, dictionary-based disambiguation is needed. Abbreviations without dots that have both vowels and consonants (both upper- and lower-case) can be confusing, and an abbreviation dictionary allows to hot-fix issues. However one may want to maintain multiple dictionaries.

Acronyms vocabulary

Acronyms - are initialisms that are pronounced rather than spelled. In other words, the purpose of this vocabulary is to contain “negative” examples that shouldn’t be classified as initialisms and should be pronounced following g2p rules for common words. Typical examples are “NASA,” “NATO,” or “AIDS.”

Cased vocabulary

Some abbreviations should be treated as such only in a specific case. For example, “US” is a country, and “us” is a first-person plural pronoun in the objective case. At first glance, only upper case is expected in abbreviations, but there might be a mix of cases, for example, “mRNA.”

Uncased vocabulary

Some abbreviations are case-independent, and it doesn’t matter which case they are written in. They should be spelled. For example, “usa,” “uk” or “iq.”


When looking at a sequence of letters, even in an unknown word, it is possible to say if it can be pronounced or should be spelled. Maintaining a comprehensive dictionary of all abbreviations is very laborious work. Thus, it is worth having an additional heuristic that would capture at least the most apparent cases of abbreviations that are not in vocab.

To define what “difficult-to-pronounce” means, it is worth collecting a list of abbreviations and comparing it with a list of common words. One can even build a classifier having such datasets, but we would be looking to keep it simple to be able to integrate such a solution into FST-based text-normalization.


Two datasets are composed and shared in this repository. One is slightly cleaner but significantly smaller, another is much larger, but likely contains annotation errors.

#1: cmudict & wiki based

The abbreviation dataset is composed by merging 3 resources: cmudict acronyms [3]; acronyms parsed from wikipedia [4]; book of acronyms and initialisms [5]. Those resources are combined, excluding duplicates and regular words, to compose a dataset of 3.3k abbreviations. As negative examples, a list of 125k words from cmudict is used.

#2: kestrel based

Kestrel[6] is a Google text normalization system. By normalizing Wikipedia, a dataset for text-normalization research was composed [7]. The dataset is not perfectly clean and certainly contains both false alarms (regular words marked as abbreviations) as well as misses (abbreviations marked as regular words). However, the quantity of the data is massive, which may outweigh the annotation mistakes. To avoid long tail of semantically unreasonable input, both abbreviations and words can be filtered by frequency. For example, taking only abbreviations/words that occur at least 4 times gives a dataset of 48k abbreviations and 688k common words.

Abbreviation n-grams

Without building a classifier, the easiest way to detect sequences of characters that are difficult to read is to count n-grams and check which are frequent for abbreviations but rare for common words. We can compose an FST acceptor, which will detect abbreviations that are not in any of the vocabularies. For both datasets, only n-grams which are never observed for common words, are selected. Below are some examples to give a taste what of kind of letter sequences are abbreviation specific. “^” - means start of word; “$” - end of it.

2-grams examples
3-grams examples
4-grams examples

Worth noting that n-gram-based detection fails miserably without a hard limit on the maximum word length. It is common in free-form text, to have concatenations of several words without whitespace. On the word boundaries, there might be very unusual letter sequences. To avoid marking those as abbreviations, we suggest considering if OOV word is an abbreviation only in case it is sufficiently short (less than 7 letters).

Detecting abbreviations

Let’s try to evaluate how often abbreviation-specific n-grams indicate abbreviation and if they can be used as reliable detectors. We compose a test set by manually annotating random examples from [7]. It also provides an idea of the quality of the kestrel-based abbreviation dataset. We can probe two use cases:

  • How often LETTERS semiotic class is missed by n-gram-based detection (approx. miss rate)
  • How often is abbreviation getting incorrectly detected for PLAIN semiotic class (approx. false alarms)

Latter happens extremely rarely. For a subset of 11M tokens, just 35 cases. Within those cases, almost all of them - are a concatenation of words (for ex. “GameGo”) or concatenation of words and abbreviations (for ex. “pacRNA”). We avoid it by applying n-gram-based abbreviation detection only to upper-case text.

For the former, the performance is summarized in the table below, where TP - true positive, MR - miss rate, FA - false alarm, TN - true negative.

100 usecases Kestrel
#1 dataset n-grams
TP / MR / FA / TN
#2 dataset n-grams
TP / MR / FA / TN
vowels+consonants marked as LETTERS semiotic class by Kestrel 87 / 13 35 / 52 / 0 / 13 28 / 59 / 0 / 13

Kestrel falsely marks few words as abbreviations (13 out of random 100):

Kestrel Abbreviation False Alarms

The n-gram-based approach generally has a high miss rate. N-grams from dataset #1: 52/100; from dataset #2: 59/100. It indicates that a list of difficult-to-pronounce n-grams needs to be completed. For example, “KQ” hardly happens in common words. It just didn’t happen in abbreviations from the dataset. N-grams selected from dataset #2 are more permissive because of a bigger overlap between common words and abbreviations. On the positive, the n-gram-based approach doesn’t produce any false alarms.

Abbreviations that doesn't contain n-grams from dataset #1

The list of n-grams that are difficult to pronounce is a good starting point for abbreviation detection. It allows us to keep abbreviation dictionaries smaller and generalize to unseen abbreviations at least somehow.

Non-alpha characters in abbreviations

One more issue that might introduce difficulties in detecting abbreviations - non-alpha characters. There are a few frequent ones that abbreviation detection should take into account.

apostrophe (‘)

Abbreviations may have a suffix that should be considered when pronunciation is produced via spelling. The most common suffixes are plural (s) or possessive marker (‘s). The acceptor for it looks as following:

s = pynini.union(pynini.accep('s'), pynini.cross('S', 's'))
suffix = pynini.accep('s') | (pynini.accep('\'') + s)
optional_suffix = pynini.closure(suffix, 0, 1)

It can be appended to any abbreviation described before.

and (&)

Ampersand is commonly used inside an abbreviation to shorten the “and” word. Typical examples are “AT&T” or “R&D.” This is relatively easy to handle because ampersand surrounded by capital letters - almost exclusively signalizes abbreviation.

and_abbr = pynini.closure(UPPER, 1) + pynini.cross("&", " and ") + pynini.closure(UPPER, 1)

hyphen (-)

An abbreviation may be connected to a common word or digits via a hyphen. For example, something already used in this post: “FST-based,” or smth commonly used for naming: “T-800”. This is more of a design issue of the text-normalization system. In general, it can be solved similarly to how ranges are handled (1990 - 1995 or 2 : 1), i.e., by allowing custom token boundaries other than space.


[1] Paul Taylor. Text-to-speech Synthesis. link

[2] K. Gorman, R. Sporat. Finite-State Text Processing. link

[3] Acronyms in CMUdict. link

[4] List of Abbreviations. Wikipedia. link

[5] A Handbook of Acronyms and Initialisms: Volume 88. link

[6] P. Ebden, R. Sproat. The Kestrel TTS text normalization system link

[7] Sproat & Jaitly (2017). Text Normalization for English, Russian and Polish. link