SPARSAR: a System for Poetry Automatic Rhythm and Style AnalyzeR

Any poem can be characterized by its rhythm which is also revealing of the poet's peculiar style. In turn, the poem's rhythm is based mainly on two elements: meter, that is distribution of stressed and unstressed syllables in the verse, presence of rhyming and other poetic devices like alliteration, assonance, consonance, enjambements, etc. which contribute to poetic form at stanza level. Traditionally, poetic meter is visualized by a sequence of signs, typically a straight line is used to indicate vowels of stressed syllabes and a half circle is positioned on vowels of unstressed ones. The sequence of these sings makes up the foot and depending on number of feet one can speak of iambic, trochaic, anapestic, dactylic, etc. poetic style. English poetry has been for centuries characterized by iambic pentameter, that is a sequence of five feet made of a couple of unstressed + stressed syllables. These signs can be then transformed into numerical sequences as for instance in Hayward (1991,96) who uses them to feed a connectionist model of poetic meter from a manually transcribed corpus, where he also tries to state the view that poets are characterized by their typical meter and rhythm, which work as their fingerprint. We also agree with this view, however, we would like to be more specific on the notion of rhythm that we intend to purport. In our view, a prosodic acoustic view needs to be implemented as well, if any precise definition of rhythm and style is the goal. Syllables are not just any combination of sounds, and their internal structure is fundamental to the nature of the poetic rhythm that will ensue. This is partly amenable to the use and exploitation of poetic devices, which we also intend to highlight in our system. But what is paramount in our description of rhythm, is the use of the acoustic parameter of duration. In our demo we will show how poems can be characterized by the use of rhythmic and stylistic features in a highly revelatory manner, by comparing metrically similar poems of the same poet. To this aim we assume that syllable acoustic identity changes as a function of three parameters: - internal structure in terms of onset and rhyme which is characterized by number consonants, consonant clusters, vowel or diphthong - position in the word, whether beginning, end or middle - primary stress, secondary stress or unstressed These data have been collected in a database called VESD (Venice English Syllable Database) to be used in the Prosodic Module of SLIM, a system for prosodic self-learning activities. Syllables have been collected from WSJCAM, the Cambridge version of the continuous speech recognition corpus produced from the Wall Street Journal, distributed by the Linguistic Data Consortium (LDC). We worked on a subset of 4165 sentences, with 70,694 words which constitute half of the total number of words in the corpus amounting to 133,080. We ended up with 113,282 syllables and 287,734 phones. The final typology is made up of 44 phones, 4393 syllable types and 11,712 word types. As far as syllables are concerned, we considered only 3409 types. From word-level and phoneme-level transcriptions we produced syllables automatically by means of a syllable parser. This work has been presented elsewhere (Bacalu and Delmonte, 1999a, 1999b). In our demo we will show haw poems can be chacterized by the use of rhythmic and stylistic features in a very revelatory manner, by comparing metrically similar poems of the same poet.