Accent is one of the issue for speech recognition
systems. Automatic Speech Recognition systems must yield high
performance for different dialects. In this work, Neutral Kannada
Automatic Speech Recognition is implemented using Kaldi
software for monophone modelling and triphone modeling. The
acoustic models are constructed using the techniques such as
monophone, triphone1, triphone2, triphone3. In triphone
modeling, grouping of interphones is performed. Feature
extraction is performed by Mel Frequency Cepstral Coefficients.
The system performance is analysed by measuring Word Error
Rate using different acoustic models. To know the robustness and
performance of the Neutral Kannada Automatic Speech
Recognition system for different dialects in Kannada, the system is
tested for North Kannada accent. Better sentence accuracy is
obtained for Neutral Kannada Automatic Speech Recognition
system and is about 90%. The performance is degraded, when
tested for North Kannada accent and the accuracy obtained is
around 77%. The performance is degraded due to the increasing
mismatch between the training and testing data set, as the Neutral
Kannada Automatic Speech Recognition system is trained only for
neutral Kannada acoustic model and doesn't include north
Kannada acoustic model. Interactive Kannada voice response
system is implemented to identify continuous Kannada speech
sentences.
Triphone Model Based Novel Kannada Continuous Speech Recognition System using Kaldi Tool
