تشخیص خطای حاشیه نویسی مبتنی بر انتزاع در کانتور گفتار سنتز

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. We propose and evaluate several anomaly detection models – Gaussian distribution based detectors, Grubbs’ test based detector, and one-class support vector machine based detector. Word-level feature sets including basic features derived from forced alignment and various acoustic, spectral, phonetic, and positional features are examined to find an optimal set of features for each anomaly detector. The results with F1 score being almost 89% show that anomaly detection could help detecting annotation errors in read-speech corpora for speech synthesis. Furthermore, dimensionality reduction techniques are also examined to automatically reduce the number of features used to describe the annotated words. We show that the automatically reduced feature sets achieve statistically similar results as the hand-crafted feature sets. We also conducted additional experiments to investigate both robustness of the proposed anomaly detection framework with respect to particular data sets used for development and evaluation and the influence of the number of examples needed for anomaly detection. 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.