We all know about transcription, right … or do we? What are the ultimate goals? What are the limits, and are they inherent ones or self-imposed ones? I’m taking this opportunity to expand on some important transcription breakthroughs in the recent STEMMA V4.1 release.
Most people would begin by transcribing textual sources paragraph-by-paragraph, or sometimes line-by-line, dependent upon the actual source. It would quickly become apparent, though, that various scenarios cannot be transcribed directly as literatim text, such as uncertain characters or words, crossed-out text, text inserted or changed, and marginal annotation. What those people then have to do is decide on some form of mark-up to represent those scenarios (see Power of Annotation), but which one?
There are many schemes, ranging from old-style manuscript mark-up, through simple ASCII-character mark-up, to full-blown mark-up languages such as TEI (Text-Encoding Initiative). This latter technology, for instance, can represent semi-diplomatic or full diplomatic transcription of textual sources to digital form. Diplomatic transcription might be valuable for preservation but is that what we need for analysis?
This should be the easiest of the cases; when given a page of typed text then we might employ OCR to automate the conversion to a digital form. This is all very well if it is perfectly readable, but barely-readable sections, or additional hand-written annotation, would require a mark-up scheme.
And yet there are some subtle, but profoundly important, situations that rarely get mentioned. The presence of different fonts or typefaces in a printed electronic document would be taken for granted as indicating some semantic difference (e.g. a heading, abstract, or a footnote), but what about documents produced on an old-style typewriter? The presence of different typefaces might then indicate that a document was written on different machines at different times. Similarly with the alignment of the lines, or the marginal indent. But how do we indicate that in the digital form?
Suppose that there was a difference in the sophistication of the grammar in different sections, one that might provide a vital clue to different authors. How would that be represented?
A more important question is who would be the beneficiary of those indications? Schemes concerned with preservation will employ software taxonomies to categorise every eventually, but those subtleties — which could be crucial to the analysis and interpretation of a document by a researcher — would almost certainly be excluded as unimportant in the digital representation.
When transcribing manuscript documents then the points I’ve just raised become much more prominent. Contributions from different authors are generally more obvious because of their handwriting styles, and these obviously need to be distinguished in order to support any analysis, but what about stylistic variations?
Suppose that someone had underlined a word. That would clearly be an indication of emphasis, and the transcriber might represent it using some mark-up language (e.g. <u>word</u>) or some lightweight mark-up language (e.g. __word__), but what if a different word was underlined twice, or more times? This question also applies to text that has been struck-out. My point is that this is an important piece of information to capture, but how much more is required for analysis than for preservation?
As another example, consider if the author had used different coloured inks. James Joyce and Virginia Woolf both used different coloured pens or crayons in their work. Should a mark-up scheme have taxonomies for the basic colours, or all possible shades and hues? Character size and intensity (e.g. from a firm hand) can also be indicative of something. Who would benefit, though, from knowing that one paragraph was in dark green and another in light green: the software or the researcher? Is there a practical limit to the number of important variations that software taxonomies can distinguish, and if so then why do we insist on that route?
Schemes that deal with audio transcription are generally specialist, and distinct from those related to textual transcription. The main reason is that those stylistic variations multiply exponentially. Not only do the transcriptions have to distinguish between contributions from different speakers, but they also need to indicate such things as speaking quickly/slowly, loudly/softly/whispered, singing, false accents, mimicry, and even different intonation. Schemes for audio transcription try to define taxonomies for these cases — although there will always be cases that aren’t covered — and the area of intonation is treated in a very formal way by linguistic analysis.
There may be cases of unknown words, slang, or strange pronunciations, each of which may need clarifying annotation.
While it is clear that the field is complex, I want to make an argument that there is a broad categorisation of the scenarios that has parallels in textual transcription, and that a single approach can deal with all three transcription source types. First, let’s look at some further complexities for audio.
There may be utterances or sounds from a given contributor that cannot be transcribed directly as text. For instance, a sneeze, cough, sniff, yawn, whistle, laugh, or swallow.
There may be a significant pause in someone’s speech that is important in the context of their words.
There may be any number of gestures or items of non-verbal communication that are equally important to capture within the transcript. For instance, a nod, smile, head-shake, squint, frown, or applause.
There may be instances where different voices — each of which is being transcribed — are overlapping each other, or where there is some untranscribed background contribution.
We can group all the above scenarios into the following broad categories:
- Language from different contributors. Distinguishing different hands, voices, etc.
- Stylistic differences from any particular contributor. Different emphasis, emotional delivery, typeface, handwriting, etc.
- Annotation where explanation or clarification is needed. Examples are unusual words, unknown words, slang, or local pronunciations.
- Contributions that cannot be transcribed directly or wholly as text. This includes changes, marginal notes, noises, gestures, and pauses.
- Parallel Contributions. This category is specifically related to audio.
STEMMA’s transcription support is designed to make material searchable, but also to support deep analysis. Some of these categories were already catered for in the cases of textual transcription, but supplementing them to cater for the remaining categories implicitly addressed audio transcription too. For instance, the <Alt> and <NoteRef> elements already catered for category #3 and needed no changes. The <Anom> element already represented textual anomalies, and so was extended to address the other anomalies in category #4.
The way that <Anom> was extended set the scene for the other extensions I will describe in a moment. Its existing taxonomy (see the http://stemma.parallaxview.co/anomaly-mode/ namespace) was given extra items of Gesture, Noise, and Pause. Within these, though, the specific gestures and noises are described using text, by and for the researcher, and not by using some limitless software taxonomy.
The STEMMA transcription elements <ts> (typescript sources) and <ms> (manuscript sources) were supplemented by <voice> (audio sources), and each were enhanced to cope with categories #1 and #2. They were extended with new attributes of ‘id’ and ‘scheme’, For instance:
<ms id=’id’ scheme=’scheme’>An example sentence</ms>
What these attributes do is attach a key representing the contributor (e.g. a hand, or a voice) and a specific stylistic variation of that contributor. There are no taxonomies used here since the differentiation and description may be subjective; the differentiation is designed to support analysis, not simply a matter of rendition; and there need to be no constraints.
The last category (#5) is addressed by specific variations of the <voice> element that allow it to be used as a container for multiple contributions.
A small example of an audio transcription employing these features may be found at Dialogue Transcription. The <ts>, <ms>, and <voice> elements are documented at Descriptive Mark-up.
The rationale behind this approach is actually quite a well-known one, although not in this field. In the area of Web mark-up, HTML5 tries to separate structure and content from presentation, the latter being left to something like CSS. For the formatting of Web pages, this avoids cluttering the mark-up describing the structure and content of page information, and ensures a consistent presentational style is applied across the pages. For transcription, it avoids cluttering the mark-up describing the structure and content from various contributors, but leaving complete freedom to the researcher to describe these in narrative as part of their analysis process.