Anomaly-based annotation error detection in speech-synthesis corpora
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43931740" target="_blank" >RIV/49777513:23520/17:43931740 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.csl.2017.04.007" target="_blank" >http://dx.doi.org/10.1016/j.csl.2017.04.007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csl.2017.04.007" target="_blank" >10.1016/j.csl.2017.04.007</a>
Alternative languages
Result language
angličtina
Original language name
Anomaly-based annotation error detection in speech-synthesis corpora
Original language description
We investigate the problem of automatic detection of annotation errors in single-speaker read-speech corpora used for speech synthesis. For the purpose of annotation error detection, we adopt an anomaly detection framework in which correctly annotated words are considered as normal examples on which the detection methods are trained. Misannotated words are then taken as anomalous examples which do not conform to normal patterns of the trained detection models. The results with F1 score being almost 89% show that anomaly detection could help detecting annotation errors in read-speech corpora for speech synthesis. We show that the automatically reduced feature sets achieve statistically similar results as the hand-crafted feature sets. We show that a reasonably good detection performance could be reached with using significantly fewer examples during the detector development phase. We also propose a concept of a voting detector – a combination of anomaly detectors in which each “single” detector “votes” on whether or not a testing word is annotated correctly, and the final decision is then made by aggregating the votes. Our results show that the voting detector has a potential to overcome each of the single anomaly detectors. Furthermore, we compare the proposed anomaly detection framework to a classification-based approach (which, unlike anomaly detection, needs to use anomalous examples during training) and we show that both approaches lead to statistically comparable results when all available anomalous examples are utilized during detector/classifier development. However, when a smaller number of anomalous examples are used, the proposed anomaly detection framework clearly outperforms the classification-based approach. A final listening test showed the effectiveness of the proposed anomaly-based annotation error detection for improving the quality of synthetic speech.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA16-04420S" target="_blank" >GA16-04420S: Combining phonetic and corpus-based approaches to remedy disruptive effects in synthetic speech</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTER SPEECH AND LANGUAGE
ISSN
0885-2308
e-ISSN
—
Volume of the periodical
46
Issue of the periodical within the volume
November
Country of publishing house
GB - UNITED KINGDOM
Number of pages
35
Pages from-to
1-35
UT code for WoS article
000407609600001
EID of the result in the Scopus database
2-s2.0-85019202074