1. Introduction
Detecting syllables in words is essential for various natural language processing tasks, including spell-checking and text-to-speech synthesis. Traditional methods like regex, hard-coded rules, and finite state automata have their limitations.
In this tutorial, we’ll explore several approaches to detecting syllables in a word. First, we’ll use the NLTK library to access the CMU Pronouncing Dictionary for syllable counting and syllabification based on phonetic transcriptions. Subsequently, we’ll examine how Pyphen can be used to divide words into syllables based on predefined rules.
2. Using NLTK
NLTK provides access to the CMU Pronouncing Dictionary, which contains phonetic transcriptions of words. By using this dictionary, we can count syllables based on phonetic transcriptions. In our approach, we’ll start by loading the CMU Pronouncing Dictionary. Next, we’ll randomly pick three words from this dictionary. For each selected word, we’ll calculate the number of syllables, count the vowels, and syllabify the word. Finally, we’ll output the results.
2.1. NLTK Example Code
Here’s the code to detect syllables:
$ cat nlkt.py
import nltk
from nltk.corpus import cmudict
import random
import pandas as pd
# Download the cmudict if not already present
nltk.download('cmudict')
d = cmudict.dict()
# Function to count the number of syllables
def nsyl(word):
return [len(list(y for y in x if y[-1].isdigit())) for x in d[word.lower()]]
# Function to syllabify the word using CMU Pronouncing Dictionary
def syllabify(word):
if word.lower() in d:
pronunciation = d[word.lower()][0]
syllables = []
current_syllable = []
for phoneme in pronunciation:
current_syllable.append(phoneme)
if phoneme[-1].isdigit(): # End of a syllable
syllables.append(current_syllable)
current_syllable = []
return '-'.join([''.join(s) for s in syllables])
return word
# Function to count the number of vowels in the word
def count_vowels(word):
return sum(1 for char in word if char.lower() in 'aeiou')
# Randomly pick 3 words from the cmudict
random_words = random.sample(list(d.keys()), 3)
syllables = []
vowels = []
syllabified_words = []
for word in random_words:
syl = nsyl(word)[0] if word.lower() in d else 0
vow = count_vowels(word)
syllables.append(syl)
vowels.append(vow)
syllabified_words.append(syllabify(word))
# Create a data frame to display the results
df = pd.DataFrame({
'word': random_words,
'syllables': syllables,
'vowels': vowels,
'syllabified_word': syllabified_words
})
print(df)
The above code uses the CMU Pronouncing Dictionary from the NLTK library to perform syllable counting and syllabification for randomly selected words.
2.2. Function Descriptions
To begin with, we download the CMU dictionary if it isn’t already available and load it into a variable named d.
We then define three primary functions to process the words. The nsyl function counts the syllables in a word by examining its phonetic transcription in the CMU dictionary. Initially, the function takes a word as input. It then looks up the word in the CMU dictionary, which returns a list of potential phonetic transcriptions.
Subsequently, for each transcription, it counts the phonemes ending with a digit, which indicates vowel sounds, reflecting the number of syllables. Ultimately, the function returns a list with the syllable counts for each word’s pronunciation.
Next, the syllabify function divides a word into its constituent syllables based on its phonetic transcription. First, this function retrieves the word’s first phonetic transcription from the CMU dictionary. After that, it initializes empty lists for syllables and the current syllable being built. As it iterates through the phonemes in the transcription, it checks if a phoneme ends with a digit, signifying the end of a syllable. When this occurs, the function appends the current syllable to the syllables list and starts a new syllable. Finally, it joins the syllables with hyphens and returns the syllabified word.
On the other hand, the count_vowels function counts the number of vowel letters (a, e, i, o, u) in a word. It begins by taking a word as input, examines each character to determine if it is a vowel, and tallies the vowel occurrences.
The script then randomly selects three words from the CMU Pronouncing Dictionary. For each selected word, it calculates the syllable count using the nsyl function, counts the vowels with the count_vowels function, and syllabifies the word using the syllabify function. These results are organized into lists and displayed in a data frame.
2.3. Word Syllabification
However, there’s an issue with the syllabified words produced. To elaborate, the output often includes phonetic notations such as NAE1 rather than properly segmented syllables. For instance, words like NAE1 and ZAH0 do not reflect accurate syllabification, highlighting a limitation in the current approach. The breakdown of words might look like this:
word syllables vowels syllabified_word
0 nasons 2 2 NAE1-SAH0
1 zabinski 3 3 ZAH0-BIH1-NSKIY0
2 paulik 2 3 PAO1-LIH0
As observed, the syllabification result contains phoneme symbols like NAE1 instead of natural syllable breaks such as na-son or za-bin-ski.
3. Using Pyphen
Alternatively, we can use Pyphen, a Python library specifically designed for hyphenation and syllabification based on language-specific rules. To elaborate, Pyphen is effective in breaking words into syllables by inserting hyphens where syllable boundaries occur, which simplifies the process of syllabification for various applications.
To elaborate on the functionality of pyphen, we set some custom words. We start by defining a list of words, such as contest, conflict, construct, table, and lion. By using pyphen, we break these words into syllables.
3.1. Example Code
Here’s how the code is structured:
$ cat pyphen.py
import pyphen
import pandas as pd
# Create a Pyphen object for English
dic = pyphen.Pyphen(lang='en')
def syllabify(word):
# Insert hyphens into the word to indicate syllable breaks
return dic.inserted(word).replace('-', ' ')
# List of custom words
words = ['contest', 'conflict', 'construct', 'table', 'lion']
# Apply syllabification to each word
syllabified_words = [syllabify(word) for word in words]
# Create a DataFrame to display the results
df = pd.DataFrame({
'word': words,
'syllabified_word': syllabified_words
})
print(df)
The above code snippet demonstrates how we’re using pyphen to syllabify a list of words.
3.2. Code Explanation
First, we import the necessary libraries and create a pyphen object configured for English. Next, we define the syllabify function, which uses Pyphen to insert hyphens into the word, replacing them with spaces for clarity in this example.
After that, we specify our custom list of words and apply the syllabify function to each word. After that, we compile the results into a data frame df. This data frame displays the original words alongside their syllabified forms.
However, it’s important to note a few things. While Pyphen provides a straightforward way to syllabify words, it doesn’t always adhere strictly to specific syllabification rules like VCCV or VCC. Instead, it relies on a pre-defined dictionary of common syllable patterns. Therefore, this method may not always follow general syllabification rules. As a result, it may not accurately reflect phonological structures:
word syllabified_word
0 contest con test
1 conflict con flict
2 construct con struct
3 table table
4 lion li on
If we need precise syllabification based on specific linguistic rules, we must develop a custom algorithm. This algorithm would be tailored to those rules. It would involve a detailed approach to analyzing and breaking down words into syllables based on specific patterns and rules.
4. Conclusion
In this article, we explored how to detect syllables in a word using different approaches. Initially, we covered the nltk method with the CMU Pronouncing Dictionary to apply phonetic rules for syllable counting and segmentation. Additionally, we discussed using pyphen for a more straightforward syllabification technique by inserting hyphens into words.
Although effective for general purposes, pyphen may not always align with specific syllabification rules. Therefore, a custom algorithm might be necessary for precise syllable segmentation based on linguistic patterns.