## 4aSC6. An application of Dempster and Shafer's probability theory to speech recognition.

### Session: Thursday Morning, December 5

### Time:

**Author: Tetsunori Kobayashi**

**Location: Dept. of EECE, Waseda Univ., 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169 Japan**

**Abstract:**

The Dempster and Shafer probability theory is applied to calculation of the
likelihood function for speech recognition. HMM is now the major technique for
speech recognition. It, however, has some limitations because it adopts the
Bayse-based likelihood function. In the Bayse theory, the values of likelihood
functions are valid only in comparative situations. The values themselves are
meaningless. Therefore, they are not applicable to spotting or branch pruning.
Besides, there is no guiding principle for merging the likelihoods from
different information sources. To solve these problems, the Dempster and Shafer
probability is adopted. The DS theory can combine likelihood functions from
multiple sources. It also treats the information of ``do not know'' or ``cannot
decide.'' The values themselves can represent the certainty of the evidence.
Here, it is adopted in three parts. First, the frame-level phonetic likelihood
functions are calculated by merging the likelihood functions from multiframe
features. Second, the segment-level phonetic likelihood functions are calculated
by merging segmental and durational likelihood functions. Finally, the phonetic
sequence is decided by using multi-segmental-level likelihood functions. All
merging processes are performed by the DS theory. Thus the new speech
recognition method, whose likelihood function is applicable to spotting and
branch pruning, is realized.

ASA 132nd meeting - Hawaii, December 1996