Multi-speaker articulatory trajectory formation based on speaker-independent articulatory HMMs
Inter-speaker variability in the speech spectrum domain has been modeled using speaker-adaptive training (SAT), in which speaker-independent phoneme-specific hidden Markov models (HMMs) were used along with a speaker-adaptive matrix. In this paper, multi-speaker articulatory trajectory formation based on this method is presented. Both speaker-independent and speaker-specific features are statistically separated from a multi-speaker articulatory database, which consists of the mid-sagittal motion data of the lips, incisor, and tongue measured with an electro-magnetic articulographic (EMA) system. We evaluated the proposed method in terms of the RMS error between the measured and estimated articulatory parameters. When multi-speaker models of articulatory parameters with two speaker-adaptive matrices for each speaker were used, the average RMS error of articulatory parameters was 1.29mm and showed no statistically significant difference from that for speaker-dependent models (1.22mm). For comparison, multi-speaker models of the conventional speech spectrum were also constructed using a multi-speaker spectrum database, which consists of speech data simultaneously recorded during the articulatory measurements. The average spectral distance between the vocal-tract and estimated spectrum from two-matrix models was 4.19dB and showed a statistically significant difference from that for speaker-dependent models (3.97dB). These results indicate that modeling of inter-speaker variability in the articulatory parameter domain with a small number of matrices for each speaker almost perfectly approximates the speaker dependency of articulation and is better than that in the speech spectrum domain.
articulatory trajectory formation articulatory hmms inter-speaker variability speaker-adaptive training