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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