Soft missing-feature mask generation for simultaneous speech recognition system in robots

This paper addresses automatic soft missing-feature mask (MFM) generation based on a leak energy estimation for a simultaneous speech recognition system. An MFM is used as a weight for probability calculation in a recognition process. In a previous work, a threshold-base-zero-or-one function was applied to decide if spectral parameter can be reliable or not for each frequency bin. The function is extended into a weighted sigmoid function which has two free parameters. In addition, a contribution ratio of static features is introduced for the probability calculation in a recognition process which static and dynamic features are input. The ratio can be implemented as a part of soft mask. The average recognition rate based on a soft MFM improved by about 5% for all directions from a conventional system based on a hard MFM. Word recognition rates improved from 70 to 80% for peripheral talkers and from 93 to 97% for front speech when speakers were 90 degrees apart.