Non-linear vector interpolation by neural network for phoneme identification in continuous speech

The correlations between vectors in a sequence of analysis frames are supposed to be specific to phonetic units in acoustic-phonetic decoding of speech. The authors propose nonlinear vector interpolation techniques to represent this correlation and to recognize phonemes. The interpolation is based on the decomposition of a frame sequence into two parts and on the construction of a function that interpolates one part using information from the second part. According to quantities to be interpolated, three families of interpolator models are developed. In a recognition system, each phonemic symbol is associated with a nonlinear vector interpolator which is trained to give minimum interpolation error for that specific phoneme. Multilayer feedforward neural networks are used to implement the nonlinear vector interpolators. For continuous speech under the phoneme spotting test using 16 PLCC-derived cepstrum coefficients as parametric vectors, the three categories of models gave compatible results