  The aim of this paper is to develop a flexible framework capable of
automatically recognizing phonetic units present in a speech utterance of any
language spoken in any mode. In this study, we considered two modes of speech:
conversation, and read modes in four Indian languages, namely, Telugu, Kannada,
Odia, and Bengali. The proposed approach consists of two stages: (1) Automatic
speech mode classification (SMC) and (2) Automatic phonetic recognition using
mode-specific multilingual phone recognition system (MPRS). In this work, the
vocal tract and excitation source features are considered for speech mode
classification (SMC) task. SMC systems are developed using multilayer
perceptron (MLP). Further, vocal tract, excitation source, and tandem features
are used to build the deep neural network (DNN)-based MPRSs. The performance of
the proposed approach is compared with mode-dependent MPRSs. Experimental
results show that the proposed approach which combines both SMC and MPRS into a
single system outperforms the baseline mode-dependent MPRSs.
