Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Voice Color Similarity for the development of HMM Based Emotional Text to Speech Synthesis

HMM 기반 감정 음성 합성기 개발을 위한 감정 음성 데이터의 음색 유사도 분석

  • Received : 2014.05.27
  • Accepted : 2014.09.11
  • Published : 2014.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Maintaining a voice color is important when compounding both the normal voice because an emotion is not expressed with various emotional voices in a single synthesizer. When a synthesizer is developed using the recording data of too many expressed emotions, a voice color cannot be maintained and each synthetic speech is can be heard like the voice of different speakers. In this paper, the speech data was recorded and the change in the voice color was analyzed to develop an emotional HMM-based speech synthesizer. To realize a speech synthesizer, a voice was recorded, and a database was built. On the other hand, a recording process is very important, particularly when realizing an emotional speech synthesizer. Monitoring is needed because it is quite difficult to define emotion and maintain a particular level. In the realized synthesizer, a normal voice and three emotional voice (Happiness, Sadness, Anger) were used, and each emotional voice consists of two levels, High/Low. To analyze the voice color of the normal voice and emotional voice, the average spectrum, which was the measured accumulated spectrum of vowels, was used and the F1(first formant) calculated by the average spectrum was compared. The voice similarity of Low-level emotional data was higher than High-level emotional data, and the proposed method can be monitored by the change in voice similarity.

하나의 합성기에서 감정이 표현되지 않는 기본 음성과 여러 감정 음성을 함께 합성하는 경우 음색을 유지하는 것이 중요해 진다. 감정이 과도하게 표현된 녹음 음성을 사용하여 합성기를 구현하는 경우 음색이 유지되지 못해 각 합성음이 서로 다른 화자의 음성처럼 들릴 수 있다. 본 논문에서는 감정 레벨을 조절하는 HMM 기반 음성 합성기를 구현하기 위해 구축한 음성데이터의 음색 변화를 분석하였다. 음성 합성기를 구현하기 위해서는 음성을 녹음하여 데이터베이스를 구축하게 되는데, 감정 음성 합성기를 구현하기 위해서는 특히 녹음 과정이 매우 중요하다. 감정을 정의하고 레벨을 유지하는 것은 매우 어렵기 때문에 모니터링이 잘 이루어져야 한다. 음성 데이터베이스는 일반 음성과 기쁨(Happiness), 슬픔(Sadness), 화남(Anger)의 감정 음성으로 구성하였고, 각 감정은 High/Low의 2가지 레벨로 구별하여 녹음하였다. 기본음성과 감정 음성의 음색 유사도 측정을 위해 대표 모음들의 각각의 스펙트럼을 누적하여 평균 스펙트럼을 구하고, 평균 스펙트럼에서 F1(제 1포만트)을 측정하였다. 감정 음성과 일반 음성의 음색 유사도는 Low-level의 감정 데이터가 High-level의 데이터 보다 우수하였고, 제안한 방법이 이러한 감정 음성의 음색 변화를 모니터링 할 수 있는 방법이 될 수 있음을 확인할 수 있었다.

Keywords

References

  1. T. Toda and K. Tokuda, "A speech parameter generation algorithm considering global variance for HMM-based speech synthesis," IEICE Transactions, vol. E90-D, no.5, 816-824(2007) DOI: http://dx.doi.org/10.1093/ietisy/e90-d.5.816
  2. Z-.H. Ling, Y. Hu, and L. Dai, "Global variance modeling on the log power spectrum of LSPs for HMM-based speech synthesis," Proc. INTERSPEECH, 825-828(2010)
  3. Z. Yan, Q. Yao, S.K. Frank, "Rich Context Modeling for High Quality HMM-Based TTS," INTERSPEECH 2009, 1755-1758(2009)
  4. J. Yamagishi, K. Onishi, T. Masuko, T. Kobayashi, "Acoustic modeling of speaking styles and emotional expressions in HMM-based speech synthesis," IEICE Trans. on Inf. & Syst., vol.E88-D, no.3, 503-509(2005) DOI: http://dx.doi.org/10.1093/ietisy/e88-d.3.502
  5. M. Isogai et al., "Recording script design for corpus-based TTS system based on coverage of various phonetic elements," Proc. ICASSP, vol. I, 301-304(2005)
  6. Seo-Bae Lee, "An Analysis of Formants Extracted from Emotional Speech and Acoustical Implications for the Emotion Recognition System and Speech Recognition System," Journal of the Korean society of speech sciences , No.3 Vol1, 45-50( 2011)
  7. D. S. Na and M. J. Bae, "A Variable Break Prediction Method using CART in a Japanese Text-to-Speech System," IEICE Trans. Inf. & Syst., Vol. E92-D, No.2, 349-352(2009) https://doi.org/10.1587/transinf.E92.D.349
  8. http://voicetext.jp/news/archives/2570