This paper proposes a new scheme of the tied-structure for constraining an HMM structure to increase training efficiency and recognition robustness. In conventional tied-structure approaches, tied parameters (or distributions, states, allophones) share the same value for decreasing model complexity. In the new framework, the tied parameters are correlated to each other rather than share the same value. To establish the appropriate correlation between model parameters, a speaker-independent initial model is trained using multiple sets of speaker-dependent data. The transfer vectors from the initial mean vectors of Gaussian distributions to the trained mean vectors are clustered to obtain sets of mean vectors that are mutually correlated across speakers. This kind of speaker-independent parameter-correlated structure can yield a lower degree-of-freedom for the model compared to the baseline speaker-independent model. Using the model with the parameter-correlated structure, speaker adaptation experiments are performed to demonstrate the higher training efficiency of the model compared to conventional speaker adaptation techniques without the correlation structure.
['Satoshi Takahashi', 'Shigeki Sagayama']
Tied-structure HMM based on parameter correlation for efficient model training