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software features & specs
Please Note: This page hasn't been updated for quite a while (sorry!). You may want to access Tyler's (2007) PWPL paper for a more recent introduction.
While still in active development, the SLAAP software has a number of features for interacting with and analyzing the sociolinguistic archive. This document seeks to highlight some of these features and explain the methods by which they function.
The NC SLAAP system is an Apache web server currently housed on a Macintosh G5 computer running Mac OS 10.4. Data are stored in a MySQL database and application pages are written in PHP.
Audio files are stored in high quality WAV format. The WAV files are used for analysis and available (either in toto or as short excerpts) for users to download. All audio that is presented to users for listening over the web, however, is converted to mp3 for faster loading and listening. The server uses the LAME mp3 encoder (http://lame.sourceforge.net/) to dynamically convert the archive quality audio files to mp3 format. Thus, the ~1.75 second sound clip presented to the user in Figure 1 has been extracted from the complete audio with Praat and then converted to mp3 with LAME - all in the background - in response to the user's accessing of the page.
At its most basic, the SLAAP software provides a simple user interface to the digitized audio archive. Designed to mimick online library card catalogues in many ways, the software provides browsable and searchable access to the collection (as in Figure 2).
Each interview event is the source of a record in the "library".
Importantly, speakers (including interviewers) are stored with their own records in a table in the database, with demographic information and references to the interviews they appear in. This allows for analyzing or searching for particular speakers in the archive (since some speakers will appear in multiple interviews). It also allows for search and analysis features that are based on demographics. Features along these lines haven't been fully developed yet, but one example of this would be a search feature that would allow users to retrieve all interviews with or transcripts of, say, Native American females, between the ages of 14 and 25.
Even though the development focus thus far hasn't centered on user interface, a number of user customizable views are available. In short, the library aspects are designed to be stream-lined, but flexible and powerful.
listen & annotate
While SLAAP uses the archive quality audio files (typically in WAV format) for analysis, as mentioned above, the software automatically generates an mp3 for every archived audio file for faster online listening. This mp3 is presented to the user through the QuickTime Plug-in Player (other players work, but much of the automation is designed around QuickTime). Users can not only listen to the audio through the interface, but can also enter annotations associated to particular timestamps in the audio file.
The transcript features are some of the most developed in the SLAAP software. The basic premise behind these features is to supply users a maximally simple, but powerful, orthographic transcription of the audio. In the SLAAP system, transcripts have become extensive versions of the annotations discussed above.
That is, they are designed to be ways of finding, indexing, and treating the audio data while minimally abstracting away from it.
Transcripts are stored in the database with each phonetic utterance comprising an entry (i.e., a line) in the database. For each utterance, the database stores an orthographic representation (the text) as well as the speaker and the start and end times of the utterance. Pauses, as well as speaker overlap, are recorded as a matter of course - since this information is derived from the start and end times of each utterance.
To get this level of temporal detail, transcripts are created in Praat using the TextGrid feature (an example of Praat with transcription TextGrids is shown in Figure 4).
A number of corpus-like analysis features are currently under development. These work in a number of different ways but (at present) all are derived from the the structure of the transcripts and their relationship to the audio (cf. the transcripts section above). These range from text-based analyses of the transcript information -
To illustrate just how these analyses are conducted, let's delve a little further into the inner workings of the pitch analysis shown in Figure 6. The server software (written in PHP) first communicates with the MySQL database to determine with transcripts the speaker appears in. It then retrieves non-blank lines (i.e. utterances; blank lines are pauses) by that speaker from the transcript that match the criteria of the user - such as having durations of a certain length (between .5 seconds and 4 seconds in the example in Figure 6) - and which have not been marked as lines to ignore. Users can remove lines that seem problematic from the analysis. For the pitch analysis, the orthographic transcription of the utterance isn't important for the software's analysis but it is displayed to the user.
Two aspects of the SLAAP system are particularly important for the analysis features. First, the fact that speakers are stored as discrete entities in the database allows for speaker-level analysis and comparison. Figure 6 showed results for "Yvonne". However, to retrieve an analysis for a different speaker, the user simply selects the speaker from the drop-down list. Second, analyses are conducted on the fly - as transcripts are added to the system for particular speakers the results of the analysis change automatically for those speakers, reflecting the new data. Presumably, the more data that gets added for a speaker, the more accurate the measurements become.
The analysis features are the most experimental aspect of the SLAAP software. They are being tested both methodologically (i.e., do they work as intended and are they accurate?) and theoretically (i.e., do they tell us anything important?) in order to determine their value. Nonetheless, they are an exciting aspect of the project.
January 9th, 2006
Cite: Kendall, Tyler (2006). Features of the North Carolina Sociolinguistic Archive and Analysis Project Software. Accessed 7/21/2018.
|With thanks to the North Carolina State University Libraries, the North Carolina Language and Life Project, and the William C. Friday Endowment at NC State University for their support.||© Tyler Kendall|
last mod: 7/21/2018