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Building a better rhyming dictionary
Posted on: 2009-08-13 08:00:00

Back in 2007, I created a rhyming engine based on the public domain Moby pronouncing dictionary. It simply reads the dictionary and looks for rhyming words by comparing the suffix of the words' pronunciations. Since that time, I have made some improvements.

rhyme any word

Using a combination of techniques from artificial intelligence, math, and linguistics, the rhyming engine can now figure out how to say any word that you enter. That means if you enter a word that is not in the dictionary, it will still be able to find some rhymes.

Rather than looking for technically perfect rhymes, it suggests words that would sound good together in song or poetry. For example, we sometimes ignore consonants, as suggested by this 1985 paper. That way, fervently will rhyme with urgently despite the v/g mismatch.

There is a legal advantage to this technique as well. Many of the standard word lists used by natural language processing researchers include words from an old edition of the Oxford dictionary, and so cannot be used for "commercial purposes". That's why both Rhymezone and Write Express have a relatively limited dictionary size. My rhyming engine can sidestep this issue, since it only needs to be seeded with a small number of words from unrestricted sources, and it can then import words in bulk, and guess the pronunciations without using any restricted content.

I couldn't resist doing some premature optimization. It uses one of my favourite data structures -- the trie. The program starts, reads the entire 260,000 word database, and completes in 60 ms on my netbook web server. It takes about 8 MB of memory. I guess that equates to about 0.48 mega-byteseconds per request.

Why is this hard?

Text to speech for English is still a hard problem to solve, and it is an active area of research. Consider the words rough, through, bough, thought, dough, cough, or photOgraph, photOgraphy, or physics, lymphatic, and loophole. In the 80's, and still today in many cases, text to speech is done by hiring specially trained linguists to develop the thousands of rules necessary to create pronunciations. It is only in the last 10 years or so that this task has been automated. My system has over 200,000 hints on how to interpret each part of a word given its context. With further refinements, this could probably be reduced to tens of thousands, which is still a lot.

Further reading

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Matt Saunders

2009-08-13 10:57:23
This is great thanks Steve - really impressed and this will be my default rhyming dictionary from now on.

An iPhone-friendly version of the site would be great in future.

Actually, I would pay £5 happily and £10 reluctantly for an offline iPhone app with the same wordlist/algorithm.


Matt ( twitter.com/matzie )

Steve Hanov

2009-08-13 11:19:39
I'm disappointed to hear this isn't iPhone friendly enough. Doesn't iPhone support javascript?

Maybe I'll break down and get a Mac for development. Still I am not sure it is worth it in terms of financial return.

Karan Bhangui

2009-08-14 01:29:34
Lovely! I can see major improvements from the last one.


2009-08-20 19:02:38
Hi, it's elctrobert from myprojectstudio.com. Thanks for the link it's a great dictionary. I'll be using it.


2009-08-29 16:52:31
To second the call for an iPhone friendly version, it occurred to me that this would be a lot of fun for my friends who like freestyle rapping if there were integrated voice recognition...if you plan on doing something like this, awesome, or else it would be great if there were an API available.

Matt Sephton

2009-10-16 14:31:47
Hi Steve, I was wondering if you'd considered providing an API for RhymeBrain? My email is my name at gmail dot com, but I will check back here when I can. Thanks

loki berry

2010-07-30 14:10:48
thank you, thank you, a million thank you's.

you have created an amazing, superior tool which i find inspirational. (educational, obligational, etc., etc.)

make that a billion thank you's.

btw, thanks !!


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