By Topic

Experiments on Cross-Language Attribute Detection and Phone Recognition With Minimal Target-Specific Training Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Sabato Marco Siniscalchi ; Faculty of Architecture and Engineering, University of Enna “Kore”, Enna, Italy ; Dau-Cheng Lyu ; Torbjørn Svendsen ; Chin-Hui Lee

A state-of-the-art automatic speech recognition (ASR) system can often achieve high accuracy for most spoken languages of interest if a large amount of speech material can be collected and used to train a set of language-specific acoustic phone models. However, designing good ASR systems with little or no language-specific speech data for resource-limited languages is still a challenging research topic. As a consequence, there has been an increasing interest in exploring knowledge sharing among a large number of languages so that a universal set of acoustic phone units can be defined to work for multiple or even for all languages. This work aims at demonstrating that a recently proposed automatic speech attribute transcription framework can play a key role in designing language-universal acoustic models by sharing speech units among all target languages at the acoustic phonetic attribute level. The language-universal acoustic models are evaluated through phone recognition. It will be shown that good cross-language attribute detection and continuous phone recognition performance can be accomplished for “unseen” languages using minimal training data from the target languages to be recognized. Furthermore, a phone-based background model (PBM) approach will be presented to improve attribute detection accuracies.

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:20 ,  Issue: 3 )