Applying Recent Advances in Speaker Recognition to the Field of Document Classification CS294-5 Class Project Kofi Boakye Andrew Hatch

speech recognizer Here are some words and shit, and some phones… iuf aweufb aijf aiuwedf aiu faiuf awiuef awiue faiuw juie ui uji iuf aiuw iuaw iuaw fiuoaw efoi iua iu dfiouas doia doifab sjkbd iuas uijos djhg wei woie oie aiouw efoiwbef oiweb foiwbe oiw efiow fojaw wue jw8i output “document” expected word/phoneme counts input speech document classifier Speaker recognition can be framed as a document classification problem Motivation

SVM-based techniques for speaker recognition ● Use relative frequencies of phone/word n-grams as features for each utterance, utt i : – Symmetry-based feature transformations: ● variance normalization: ● rank normalization (Andreas Stolcke): – Kernelized log-likelihood ratio (Campbell et al., NIPS 2003):

The Task 20 Newsgroups: Data set consists of 20,000 articles partitioned nearly evenly across 20 Usenet newsgroups Examples: alt.atheism, comp.windows.x, talk.politics.misc For evaluation, data is divided into training (60%) and test (40%) Speaker recognition paradigm adopted for evaluation Obtain true/false decisions for test/target pairs Each document tested against all 20 topics Summary statistic is equal error rate (EER)

The Process SVM classifier used with unigram statistics as features Unigrams selected from a ranked list according to TFIDF score(w) = p(w) * log(N/d(w)) Experimented with: Vocabulary size Smoothed (Good-Turing) counts One-versus-All approach to classifier training taken Experimented with various scaling factors and normalizations for feature vectors Variance, rank, kernelized LLR

Results Note: Present results only from alt.atheism Smoothing yielded no performance improvement Vocabulary expansion made asymptotic gains Inclusion of word stems permitted gains beyond apparent asymptote

Future Work Expand analysis to documents from all topics More sophisticated stemming Reworking of smoothing Multiclass SVMs