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Presentation #8
Session:ASR IV
Session Time:Friday, December 21, 13:30 - 15:30
Presentation Time:Friday, December 21, 13:30 - 15:30
Presentation: Poster
Topic: Speech recognition and synthesis:
Paper Title: A K-NEAREST NEIGHBOURS APPROACH TO UNSUPERVISED SPOKEN TERM DISCOVERY
Authors: Alexis Thual; ENS 
 Corentin Dancette; ENS 
 Julien Karadayi; ENS 
 Juan Benjumea; ENS 
 Emmanuel Dupoux; ENS 
Abstract: Unsupervised spoken term discovery is the task of finding recurrent acoustic patterns in speech without any annotations. Current approaches consists of two steps: (1) discovering similar patterns in speech, and (2) partitioning those pairs of acoustic tokens using graph clustering methods. We propose a new approach for the first step. Previous systems used various approximation algorithms to make the search tractable on large amounts of data. Our approach is based on an optimized k-nearest neighbours (KNN) search coupled with a fixed word embedding algorithm. The results show that the KNN algorithm is robust across languages, consistently outperforms the DTW-based baseline, and is competitive with current state-of-the-art spoken term discovery systems.