tl;dr: Given a string of text in some language you might want to know how long it would take to speak it. Here are some perl/python "one-liners" to estimate that. Currently supports English (US), German, French, Italian, Spanish and Japanese. The estimates are all in seconds.
Brave are those who read the source code. I promise it's not intentionally obfuscated -- there's just a lot of unicoding going on, and then copious use of the right-handed saturn operator in perl.
Why would you want code for this rather than a dictionary? Dictionaries are limited to their vocabulary, which is also sensitive to things like tokenization. Having a completely parametric solution seemed much more generalizable.
Why would I possibly want this?
We've been doing a bunch of work recently on simultaneous machine interpretation (aka "real time machine translation"). None of us is a speech person, either in the "recognition" or "synthesis" sense. This unfortunately means that to date, all of our models treat each word as "equally long" when training and, perhaps more importantly, evaluating models. For instance, if we want to measure the décalage (aka time-lag, aka ear-voice-span) between when a Japanese word is "heard" and the corresponding English translation is "spoken", we've been assuming all words take precisely 1 second to speak. This is obviously ridiculous.
One quick and dirty alternative is to use a text-to-speech (aka speech synthesis) system to synthesize a sentence and use its length as an estimate of how long it would take a person to speak it. This is a totally plausible approach since decent open source synthesis software exists (we use such software to crease these one-liners), but it's slow and bloated and can't be easily distributed. This would be more accurate, but I was after a quick and filthy solution.
How do these scripts work?
There are two functions: one for estimating the amount of time it would take to speak a single word (sayWord in the code), and another for estimating the amount of time it would take to speak a sentence (sayit in the code). I'll first describe how sayWord works; sayit is pretty straightforward.
sayWord works by extracting a bunch of features from the word to be spoken (each of these is a particular regular expression) and evaluating a linear function of the counts of the matches of those regular expressions. The process by which coefficients were generated is explained later. The features are things like: number of characters, number of vowels, number of consonant groups, number of non-letters, number of vowel-consonant switches, number of digits of various lengths, and whether the word starts or ends with a vowel; and then, for each "reasonable" unicode character for European languages, the count of those characters. Not all of these features appears for each language because I used l1 regularization to prune down the feature set.
[Note: Japanese is different because it uses a different character set. The feature structure is basically the same, but replace "consonant" with "kana" and "vowel" with "kanji" and "reasonable character" with "each of the 100 most frequent Japanese characters" in Japanese Wikipedia.]
sayit attempts to pronounce a sentence by pronouncing each word individually (via sayWord) and then rescaling the resulting estimate because we ... don't ... pause ... between ... words. This rescaling is linear, and again estimated from data (explained below).
How are the coefficients estimated for words?
Basically I take a vocabulary of the 50k-100k most frequent words for each language, use a speech synthesis program to say them (for the European languages, I used MaryTTS; for Japanese I used Open JTalk).
I then extract all the features mentioned above, create a regression problem regressing on the number of seconds it takes to speak (after removing quiet time around the word) and throwing in some l1 regularization with vw to make sure that it didn't use too many features. I optimized quantile loss (aka absolute value loss) rather than squared loss.
One thing I did not do was weight the words by their frequency. I could have done this and the resulting regression weights change a bit but not too much. At the end it spends a lot of energy making sure it estimates the speaking time of "a" and "the" and "an" correctly. Because short words are typically high frequency (and vice versa) this meant that it tended to underestimate all other words. And because my relatively frequencies were from Wikipedia, they might not match yours. Keeping a uniform distribution felt like a better solution.
I also tried not regularizing and also using things like character ngram features to get a better fit. In the end I didn't include this either. You can about halve the error rate by doing these things, but I felt like having simpler, smaller models made more sense here. After all, the thing we're regressing on (speaking time from a TTS system) is sort of an artificial benchmark anyway, so getting a bit lower error is not obviously that meaningful.
Overall the mean absolute errors in prediction are:
- German: 51ms
- English: 55ms
- French: 59ms
- Italian: 41ms
- Japanese: 33ms
How are the coefficients estimated for sentences?
For sentences, I fit a model of the form:
- time = const + a * [summed time for words] + b * [# of words]
Chances are no one except me wants this. But if you do, please feel free to use it. I'd appreciate some sort of credit though :). And if you really want the dictionaries, you can find them here: de, en-US, fr, it, ja.
Thanks to Graham Neubig for providing the tokenized Japanese Wikipedia text and pointing me to Open JTalk!