Flexible Voice Morphing: A Deep Dive into AR-HMM Analysis

1. G.S.MOZE COLLEGE OF ENGINNERINGBALEWADI,PUNE -45. A PRESENTATION ON Voice Morphing PROJECT GUIDE : By: Anil Mahadik Prof. SonaliGhote

2. Content • Title • Introduction • History • Need of Vocal track area function • Vocal track area function • AR-HMM Analysis • AR-HMM Diagram • Re-synthesis of Converted voice

3. Training Phase • Conversion and morphing phase • Application • Conclusion • References

4. Title • The Project title is “Voice Morphing”. • Give the information about Flexible Voice Morphing based on linear combination of multispeakers’ vocal tract area function. • Voice morphing or voice conversion usually means transformation from a source speaker’s speech to a target speaker’s.

5. Introduction • The main goal of the developed audio morphing methods is the smooth transformation from one sound to another. • These techniques are considered to be a kind of point-to-point mapping in a feature space. • There are many applications which may benefit from this sort of technology. • Research on voice morphing aims to extend this restriction to area-to-area mapping by introducing multi-speakers .

6. History • Voice morphing is a technology developed at the Los Alamos National Laboratory in New Mexico, USA by George Papcun and publicly demonstrated in 1999. • Voice morphing enables speech patterns to be cloned and an accurate copy of a person's voice be made which can then say anything the operator wishes it to say.

7. Need of Vocal track area function • Since the 1990s, many techniques for voice conver-sion have been proposed [1-7]. • One successful technique is to use a statistical method for mapping a source speaker’s voice to a target speaker’s but a weakness of these methods is the discontinuity of formants. • The proposed method employs an estimated vocal tract area function to avoid such weakness.

8. Vocal Tract area function(A) • Interpolation in the vocal tract area domain is considered to provide reasonably continuous transition of formants. • Estimation of the vocal tract area function implies simultaneous estimation of the voice source characteristics.

9. AR-HMM analysis • For this purpose of Estimation of the vocal tract area function introduce Auto-Regressive Hidden Markov Model (AR-HMM) analysis of speech. • The AR-HMM model represents the vocal tract characteristics by an AR model and the glottal source wave by an HMM. • The AR-HMM analysis estimates the vocal tract resonance characteristics and vocal source waves in the sense of maximum likelihood estimation.

10. Diagram of AR-HMM

12. Re-synthesis of the converted voice • There are two phase’s Training phase and Conversion & Morphing phase. • The procedure of each phase is as follow in Diagram.

13. Training phase • AR-HMM analysis: Speech samples with the same phonetic content from both source and target speaker are analyzed . • Feature alignment: The feature vectors obtained above are time-aligned using dynamic time warping (DTW) in order to compensate for any differences in duration between source and target utterances. • Estimation of the conversion function: The aligned vectors are used to train a joint GMM whose parameters are then used to construct a stochastic conversion function.

14. Training phase

15. Conversion and morphing phase • AR-HMM analysis: In this case only the source speaker’s utterances are used. • Features Transformation: The GMM-based transfor-mation function constructed during training is now used for converting every source log vocal tract area function and vocal cord cepstrum into its most likely target equivalent. • Linear Interpolation ,Synthesis of the source wave and LPC synthesis.

16. Conversion and morphing phase

17. Application • Applications as the creation of peculiar voices in animation films. • Voice morphing has tremendous possibilities in military psychological warfare and subversion. • Voice morphing is a powerful battlefield weapon which can be used to provide fake orders to the enemy's troops, appearing to come from their own commanders.

18. Conclusion • This paper has presented a voice morphing method based on mappings in the vocal tract area space and glottal source wave spectrum that can each be independently mod-ified. • These features have been realized using AR-HMM analysis of speech. • In future, we will investigate how to improve the quality of voice conversion with interpolation techniques.

19. References • [1] L.M. Arslan, D.Talkin, ”Voice conversion by codebook map-ping of line spectral frequencies and excitation spectrum,” Proc. Eurospeech, pp.1347-1350, 1997. • [2] Y.Stylianou, O.Cappe, “A system voice conversion based on probabilistic classification and a harmonic plus noise mod-el”, Proc.ICASSP, pp.281-284, 1998 . • [3] A.Kain, “Spectral voice conversion for text-to-speech syn-thesis”, Proc.ICASSP pp.285-288, 1998. • [4] H. Ye, S. Young, “High Quality Voice Morphing”, in Proc.IEEEICASSP, pp.9-12, 2004.