Mining a year of speech: the datasets

We shall use a collection of transcribed audio corpora which we already have at Penn, Oxford and the BL, with metadata and other annotations. One portion will be sound files published by the Linguistic Data Consortium (LDC), whose catalog now includes about 4,200 hours (almost six months) of recorded and transcribed American English speech, and more than 2,000 hours of as-yet unpublished material, including broadcast conversations such as talk shows, Supreme Court oral arguments, political speeches and debates, and audiobooks. For British English, the largest collection of transcribed spoken audio in existence is the spoken part of the British National Corpus. Although the BNC contains nearly 10 million words of transcribed speech, only about half of this (3 months) is available in audio format. Although the details vary from corpus to corpus, the transcriptions are typically at the word level; the linguistic annotations and metadata are stored in XML data structures.

American English sound files at the LDC. The transcribed American English speech in the LDC's current catalog includes about 2,240 hours of two-party telephone conversations, 260 hours of task-oriented dialogs, 100 hours of group meetings, 1,255 hours of broadcast news, 30 hours of voice mail, and 300 hours of read or prompted speech. As-yet unpublished LDC data includes about 1,000 hours of broadcast conversations (such as talk shows), and about 5,000 hours of U.S. Supreme Court oral arguments. The process of aligning all of this at the word and phonetic-segment level is under way, using the automatic forced-alignment techniques developed at Penn's Phonetics Lab. We are in the process of adding a large sample of political speeches and debates, and a collection of open-access audiobooks from librivox.org.

British English sound files in the BNC. In a pilot project we digitized 3% of the BNC recordings to determine that the audio quality is sufficient for the Penn Phonetics Lab Forced Aligner and to quantify the computational requirements. As part of Oxford University and the BL's institutional contributions to this project, the British Library Sound Archive (where the tapes are deposited) shall have digitized at least 23% by the start of the project, with the likelihood that a further 57% will be digitized using funding obtained from other sources. We already have access to the LDC and BNC data, rights to exploit it for our research, and rights to license it for research use. As licensing and other constraints allow, we aim to support scholars from other fields by including oral history recordings, newsreels, and other sources.

As an outcome of the project, we will create a new, Extended release of the BNC, in two parts: the existing text transcriptions, plus an additional structure of timing information that this project will produce. The text and timing information will be released in XML form by the BNC Consortium. We expect to make it available under the same license and via the same arrangements as the current text version of the BNC (http://www.natcorp.ox.ac.uk/). This XML data will contain beginning and ending times of words and phonemes, along with British Library recording ID numbers.

The British Library cannot publish the unprocessed, original audio, because the volunteers who collected the recordings were promised anonymity. Personal and place names are mentioned in the audio files, so these need to be deleted. We shall therefore anonymize the audio files and return them to the British Library, who will then prepare them for release and serve them via their Archival Sound Recordings project (http://sounds.bl.uk/TextPage.aspx?page=projectinfo). Fortunately, the text BNC corpus has already identified these words and replaced them with XML <gap> tags. Once we have aligned the text with the audio, we can simply mute the audio corresponding to the <gap> tags. This procedure was previously employed in the fraction of the BNC already published as the COLT corpus (http://torvald.aksis.uib.no/colt/).

This dataset is several hundred times larger than the largest datasets previously used in research of this type; and just as important, it is much more diverse. Both the size and the diversity will make new kinds of research possible. But the most important thing of all is that the framework and tools that we are developing can easily be applied to any additional material for which both recordings and (standard orthographic) transcriptions exist. As a result, others will be able to apply our methods to new datasets of interest to them, including (for example) sociolinguistic interviews, oral histories, courtroom recordings, political speeches and debates, doctor-patient interactions, and so on.