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CSL
2016
Springer

ALISA: An automatic lightly supervised speech segmentation and alignment tool

4 years 3 months ago
ALISA: An automatic lightly supervised speech segmentation and alignment tool
This paper describes the ALISA tool, which implements a lightly supervised method for sentence-level alignment of speech with imperfect transcripts. Its intended use is to enable the creation of new speech corpora from a multitude of resources in a language-independent fashion, thus avoiding the need to record or transcribe speech data. The method is designed so that it requires minimum user intervention and expert knowledge, and it is able to align data in languages which employ alphabetic scripts. It comprises a GMM-based voice activity detector and a highly constrained grapheme-based speech aligner. The method is evaluated objectively against a gold standard segmentation and transcription, as well as subjectively through building and testing speech synthesis systems from the retrieved data. Results show that on average, 70% of the original data is correctly aligned, with a word error rate of less than 0.5%. In one case, subjective listening tests show a statistically significant p...
Adriana Stan, Yoshitaka Mamiya, Junichi Yamagishi,
Added 01 Apr 2016
Updated 01 Apr 2016
Type Journal
Year 2016
Where CSL
Authors Adriana Stan, Yoshitaka Mamiya, Junichi Yamagishi, Peter Bell 0001, Oliver Watts, Robert A. J. Clark, Simon King
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