Segment selection method based on tonal validity evaluation using machine learning for concatenattve speech synthesis

This paper proposes a speech segment selection method based on machine learning for concatenative speech synthesis systems. The proposed method has two novel features. One is its use of Support Vector Machine (SVM) to estimate the subjective correctness of pitch accent with respect to each accent phrase of possible candidate speech segments. The other is its use of a determination function to identify the best segment based on SVM output. The determination function involves two assessments; one counts the number of each sign of SVM output and the other compares the distance values. The sign of SVM output is generally used to classify target objects, but the distance SVM output also represents important information. An experiment that assesses SVM performance for Japanese accent validity shows that its accuracy is 81%. To confirm the effectiveness of the proposed segment selection method, preference tests are conducted. The test indicates that the proposed method can yield Japanese synthesized speech with more natural intonation than the conventional method that uses only target cost and concatenation cost.