## 1aSC21. Automatic generation of word models using piecewise linear segment lattices.

### Session: Monday Morning, December 2

### Time:

**Author: Hiroaki Kojima**

**Location: Electrotechnical Lab., 1-1-4 Umizono, Tsukuba, Ibaraki, 305 Japan**

**Author: Kazuyo Tanaka**

**Location: Electrotechnical Lab., 1-1-4 Umizono, Tsukuba, Ibaraki, 305 Japan**

**Abstract:**

A framework for ``phonological concept formation'' has been proposed,
aiming to generate robust speech recognition models [Kojima et al., Proc. ICSLP
92, Vol. 1, pp. 269--272 (1992)]. For this purpose, a ``piecewise linear segment
lattice'' model is proposed. The structure is represented as a lattice of
segments, each of which is represented as regression coefficients of feature
vectors within the segment. Compared with typical stochastic models like HMM,
the advantages are: (1) It needs fewer samples to learn; (2) it represents
objects in voluntary precision; and (3) its structure can be dynamically changed
by less calculation. An outline of the generation algorithm is as follows: (1)
Dividing each sample into segments using DP, where the number of segments is
decided based on an MDL-like criterion; (2) matching between the sequences of
segments within the same word by DP; (3) modifying the division according to
their matching scores; (4) picking up similar (i.e., near) subsequences and
gathering them into a phonelike cluster. Speaker-independent isolated word
recognition is carried out using the proposed models which are generated in
several conditions. The results show that the recognition rate is improved by
forming phonelike clusters.

ASA 132nd meeting - Hawaii, December 1996