NDLI: Investigation of supervised dimensionality reduction methods for phonetic classification

Content Provider	ACM Digital Library
Author	Boves, Lou Liu, Yang Huang, Heyun
Abstract	Automatic Speech Recognition (ASR) depends crucially on establishing acoustic models for speech units including phones. One disadvantage that lies in popular acoustic models is the lack of modeling speech continuity information. Stacking short-term features of consecutive frames may keep sufficient articulatory information. Unfortunately, the resultant high-dimensional feature space is still full of redundant information and also causes the curse of dimensionality for subsequent acoustic modeling. Motivated by this and some recent research [4, 15], our paper investigates the supervised dimensionality reduction methods to answer two research questions: whether local structures exist in the feature space formulated by stacking frames and whether the local structures help the acoustic modeling. Experimental results by TIMIT phonetic classification show that the assumed local structures do exist in the feature space and could be best described by nearest neighbor graphs.
Starting Page	128
Ending Page	133
Page Count	6
File Format	PDF
ISBN	9781450309189
DOI	10.1145/2043674.2043712
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-08-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Local structure Supervised dimensionality reduction Timit Phonetic classification Automatic speech recognition
Content Type	Text
Resource Type	Article

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Investigation of supervised dimensionality reduction methods for phonetic classification