In his 1974 article “Sound-Patterns in Homer,” David W. Packard compared a wide range of critical opinions about the artistic use of sound in the poetics of the Iliad and Odyssey with a statistical analysis of letter frequencies. This is a seminal paper in digital humanities not only because Packard was a pioneer in designing the hardware and software necessary to digitize ancient Greek texts, but also because it addresses the interface between empirical data and critical interpretation, a problem that persists forty years on, despite huge advances in many areas of the field.
In the DHIB Textual Analysis Working Group, projects such as Tesserae attempt to adapt for the humanistic goals of literary criticism methods designed for such cold-blooded forensic purposes as authorship attribution and plagiarism detection. This means not only digitizing and analyzing, but also being able to return from statistics and data to subjective appreciation, and creating new value for readers. Here I want to show some preliminary results from my dissertation research, which benefits greatly from the intellectual cross-fertilization among the various efforts of Text Analysis. I’ll draw some parallels to Packard’s work, trying to emphasize methods that I hope show the potential for digital interpretation as well as digital analysis of literary works.
The Iliad and Odyssey are, in one way or another, the products of a long oral tradition. Despite the uncertainty that intervening changes in both pronunciation and spelling impose on any understanding we can have of these poems’ first-millennium realization, it’s clear that sound was a vital component of their composition and appreciation. Packard was primarily investigating the question of whether sound patterns were the result of deliberate poetic artistry, but others have argued that they may have served an unconscious mnemonic role, allowing illiterate singers to store vast texts in memory using a sort of data compression.
In either case, digital analysis can aid us by providing the statistics to test theories about what sort of patterns exist. But can it also help us “read” the sounds of the poem in new ways, perhaps pointing us to new hypotheses we wouldn’t otherwise have formed?
Digital Analysis
Following Packard, I begin by breaking the poems down into an alphabet of sounds, most of which have one-to-one correspondence with orthographic characters. From these atoms we can work up hierarchically to lines, either via words and n-grams, or via syllables and feet. But for now, let’s just consider the sounds themselves. The question I want to examine is, do some sounds show an interesting distribution in the poems, and, if so, what does that look like?
I downloaded the texts of the Iliad and the Odyssey from the Perseus Digital Library, concatenated them, then split them into 20-line samples. In order to get a feel for what kind of variation you might expect to see by chance alone, I created a control set where the lines of the two poems were randomly shuffled before splitting into 20-line samples. In fact, I did that ten different times. These ten control sets, then, represent a sort of background noise against which any pattern must clearly distinguish itself.
The graph below looks at the distribution of every unique pair of adjacent sounds that occurs in the two poems. The y-axis shows the portion of all samples in which a pair is found. Sound-pairs are ranged along the x-axis from most common (on average across the ten control sets) at the left, to least common at the right. The most common sound pairs occur in all samples, the least common in only one or two.
There are ten superimposed red curves, one for each of the control sets. The black curve represents the poem in its proper order. You can see that the black falls away from the red in places. Here, a sound-pair is found in rather fewer samples than you’d expect by chance alone. This means that in the original version it’s clumping up in some samples, leaving others bare.
Here’s a close-up showing two prime candidates for interesting behavior, hι and δυ. (I transliterated initial /h/ with a Latin “h” because it has no Greek letter.)
While this chart gives us a clue about which sounds might be interesting, it is a far cry from “interpretable” in a literary sense. Packard’s approach is similar. He begins with a chart showing, for each sound, the number of lines in which it does not occur at all, the number of lines in which it occurs once, twice, and so on (e.g. his Table 1). In another giant table, he lists all the lines in which a given sound occurs unusually frequently (e.g. his Table 3).
These tables serve two functions for Packard. First, where a critic has claimed that a particular line is notable for the density of some sound or other, Packard can tell at a glance how many and which other lines share the same characteristic. Second, he can survey the most “interesting” single lines to see whether they tend to be particularly charged with literary significance. But can these data be reintegrated into a new reading? Can computational techniques be turned from analysis to interpretation?
Digital Interpretation
Packard makes an exciting attempt in this direction, although he cautions that as it stands it is overly simplistic, undertaken “purely as an experiment.” He turns to the work of Dionysius of Halicarnassus, a scholar of the first century BCE who assessed the relative “harshness” of every letter of the Greek alphabet and used this as the basis for poetic criticism. Assigning to every sound a numerical value based on Dionysius’ rankings, Packard calculates for every line in the Iliad and Odyssey a “Dionysian” harshness metric.
My approach to reintegrating sound frequencies into a subjective appreciation of the larger poem draws on techniques I used when I studied satellite image processing in the Earth and Environmental Science department at Lehigh University. There we would visualize three variables from a larger set simultaneously by assigning them to red, green, and blue intensities respectively. In the following figures, each square represents twenty lines of text. The texts proceed from left to right, top to bottom, beginning with the first line of the Iliad.
In this first image, the red value represents density of the sound-pair hι, green represents ιπ, and blue represents ππ. These sounds are all components of the word ἵππος, “horse,” and the biggest bright stripe (a little more than halfway down, on the left) represents the chariot race in Iliad Book 23. Compare the picture above with the one below, made in the same way but using the first control set.
The control set shows the same variability among samples, but no large-scale patterns like the bright stripe in the first picture.
In my first experiment, the three variables used to create the colors tended to co-vary, being part of the same relatively common word. In the next example, they show more independence. Here I used sound triplets: red shows the density of the string δυσ, green represents χιλ, and blue represents τυδ. The frequency of these strings are dominated by the presence of three main characters, Odysseus, Achilles, and Diomedes (“son of Tydeus”).
The huge red region at the bottom is books 5-24 of the Odyssey. The green region in the middle is where Achilles returns to the fighting in the later part of the Iliad. Near the beginning is a blue section corresponding to the Aristeia of Diomedes.
For now, this analysis remains relatively crude, and limited to showing content-driven patterns in sound, rather than purely stylistic ones. My original aim was to perform principal components analysis on all the sound frequencies together, then assign the three color intensities to the first three principal components. So far, though, it’s turned up nothing appreciably different from what you see in the control sets.
Instead, let me close with a tribute to Packard’s approach. Here I’ve calculated his “Dionysian” score for each of my samples and assigned it to a grey scale value. Brighter samples are harsher sounding, to Dionysius of Halicarnassus’ ear, at any rate, while the black squares represent the most mellifluous passages.
But Packard’s metric was designed to examine the sound of individual lines. Perhaps it would be better read in this way:
The graphs above were made using R, the other pictures, using Processing. I used Perl for everything in between. I’d appreciate advice/comments on any aspect of this from one and all…
Originally posted to the DHIB blog.