The 37th Language Lunch
Date: 2013-05-09
Location: G.07 Informatics Forum
Where are the Challenges in Speaker Diarization?
Mark,Sinclair; Informatics; M.Sinclair-7@sms.ed.ac.uk
Speaker Diarization involves segmenting audio into speaker homogenous regions and labelling regions from each individual speaker with a single label. Knowing both who spoke and when has many useful applications and can form part of a rich transcription of speech. The task is challenging because it is generally performed without any a priori knowledge about the speakers present or even how many speakers there are.rnWe present a study on the contributions to Diarization Error Rate by the various components of a state-of-the-art speaker diarization system. Following on from an earlier study by Huijbregts and Wooters, we extend into more areas and draw somewhat different conclusions. From a series of experiments combining real, oracle and ideal system components, we are able to conclude that the primary cause of error in diarization is the training of speaker models on impure data, something that is in fact done in every current system. We conclude by suggesting ways to improve future systems, including a focus on training the speaker models from smaller quantities of pure data instead of all the data, as is currently done.
The Consequences of the Loss of Verb-Second for Information Structure in English
Bettelou,Los; PPLS; blos@staffmail.ed.ac.uk
Information Structure fits syntax like a glove in Old English. There are at least four positions for subjects, five for objects and three for adverbials, which greatly facilitate the information flow from “given” to “new”. The change from OV to VO order (ca. 1200) and the loss of a verb-second-like movement rule (15th C) greatly restricted the positions of subjects, objects and adverbials, with information status increasingly aligned with a syntactic function: subjects came to be the default expression of “given” information, and objects of “new” information. Investigating such shifts requires annotating historical texts with information structural categories. As information structure is a relatively new field, pilot schemes to enrich corpora witch labels such as “topic” or “focus” tend to have poor interrelater-agreement. This is why we have opted to add referential information only. NPs are semi-automatically marked up with referential information, with the addition of a label specifying the type of referential link, based on Prince’s (1981) givenness categories (“identity”, “inferred”, “assumed”, “inert” and “new”). In my talk I will use this corpus to investigate the hypothesis that the loss of verb-second entailed the loss of clause-initial adverbials as unmarked discourse links.
New Methods in Corpus Analysis
Christoph,Hesse; PPLS; S0975727@sms.ed.ac.uk
I present two new techniques in the field of corpus analysis. The first technique allows us to compare frequency counts across different corpora and even languages by using an approach similar to z-scores. The second technique allows us to determine for any phenomenon in a corpus how much of its frequency variation is due to true linguistic variation and how much is due to observational deficiencies (measurement accuracy and the Fourier/Heisenberg/Schrödinger/Gabor uncertainty principle).
Language Evolution
Bill,Thompson; PPLS; Bill@ling.ed.ac.uk
