Domain-specific machine translation (MT) systems are essential in bridging the communication gap between people across different businesses, economies, and countries. India, a linguistically rich country with a booming tourism industry is a perfect market for such an MT system. On this note, the current work aims to develop a domain-specific transformer-based MT system for Hindi-to-Tamil translation. In the current work, neural-based MT (NMT) model is trained from scratch and the hyper-parameters of the model architecture are modified to analyze its effect on the translation performance. Further, a finetuning approach is adopted to finetune a pretrained transformer MT model to better suit the tourism domain. The proposed experiments are observed to improve the BLEU scores of the translation system by a maximum of 1% and 4% for the training from scratch and finetuned systems respectively.

Dysarthria is a neurological motor disorder caused by cranial damage that interferes with the muscles involved in the correct pronunciation of sounds and intelligible speech. Computer Aided Pronunciation training (CAPT) systems traditionally used for the pronunciation assessment of L2 language learners can offer a method to detect and score mispronounced sounds in dysarthric speakers as a way of evaluation without human intervention. In this work, a phonetic level DNN-HMM based Goodness of Pronunciation (GoP) for pronunciation scoring, on native Tamil Dysarthric speakers corpus is presented. The scores are calculated using the posteriors of the subphonemic elements called senones with a focus on their prevalence across phones and their transitions across HMM states. The phonetic-level scores obtained for speakers of different levels of severity help establish speaker-specific trends in pronunciation through an objective log-likelihood metric, in contrast to subjective evaluations by Speech Language Therapists (SLTs).

Automatic detection and classification of dysarthria severity from speech provides a non-invasive and efficient diagnostic tool, offering clinicians valuable insights to guide treatment and therapy decisions. Our study evaluated two pre-trained models—wav2vec2-BASE and distilALHuBERT, for feature extraction to build speech detection and severity-level classification systems for dysarthric speech. We conducted experiments on the TDSC dataset using two approaches: a machine learning model (support vector machine, SVM) and a deep learning model (convolutional neural network, CNN). Our findings showed that features derived from distilALHuBERT significantly outperformed those from wav2vec2-BASE in both dysarthric speech detection and severity classification tasks. Notably, the distilALHuBERT features achieved 99% accuracy in automatic detection and 95% accuracy in severity classification, surpassing the performance of wav2vec2 features.