Articulatory-feature-based methods for performance improvement of Multilingual Phone Recognition Systems using Indian languages

Abstract

In this work, the performance of Multilingual Phone Recognition System (Multi-PRS) is improved using articulatory features (AFs). Four Indian languages – Kannada, Telugu, Bengali and Odia – are used for developing Multi-PRS. The transcription is derived using international phonetic alphabets (IPAs). Multi-PRS is trained using hidden Markov models and the state-of-the-art Deep Neural Networks (DNNs). AFs for five AF groups – place, manner, roundness, frontness and height – are predicted from Mel-frequency cepstral coefficients (MFCCs) using DNNs. The oracle AFs, which are derived from the ground truth IPA transcriptions, are used to set the best performance realizable by the predicted AFs. The performances of predicted and oracle AFs are compared. In addition to the AFs, the phone posteriors are explored to further boost the performance of Multi-PRS. Multi-task learning is explored to improve the prediction accuracy of AFs and thereby reduce the Phone Error Rates (PERs) of Multi-PRSs. Fusion of AFs is done using two approaches: i) lattice re-scoring approach and ii) AFs as tandem features. We show that oracle AFs by feature fusion with MFCCs offer a remarkably low target of PER of 10.4%, which is 24.7% absolute reduction compared with baseline Multi-PRS with MFCCs alone. The best performing system using predicted AFs has shown 3.2% reduction in absolute PER (9.1% reduction in relative PER) compared with baseline Multi-PRS. The best performance is obtained using the tandem approach for fusion of various AFs and phone posteriors.