Gradient Boost algorithms for Modelling Malayalam Poem Syllable Duration: Gradient Boost algorithms for Modelling Malayalam Poem Syllable Duration - M.P, Jasir; Mullayil, Kannan Balakrishnan; Jaseena K.U (INFOCOMP Journal of Computer Science, 2022)
Emulating natural speech has been a top priority ever since the research activities began in the area of Natural Language Processing (NLP). Text To Speech Synthesis (TTS) consists of several stages, which include Text Normalization, Syllabification and Unit Selection, Duration Analysis Modelling, and Prosody Analysis Modelling. Proper syllabification was required earlier when rule-based concatenative synthesis was used as the main method to synthesize speech. Now statistical parametric speech synthesis is the state of the art. Supervised and unsupervised machine learning frameworks can be used to model different aspects of speech such as duration, prosody etc. The proposed work uses classical poem construct Vruta (meter) to identify the features determine syllable duration. Nineteen features are extracted from the orthographic representation of poem according to the Vruta definition. Kakali, Keka, and Manjari are the Vrutas considered. Also the contextual features of the syllables and the accoustic properties like the origin of the syllable are considered to build the feature set. The proposed work employs Gradient Boost Algorithms for modelling the duration of Malayalam poem syllables. All the models give superior values for the coefficient of determination (R2) compared to other major models. Simple Gradient Boost Machine (GBM) is able to produce 90.723 for R2. Similarly, XGBoost gives 90.726, LightBoost yields 90.693 and CatBoost delivers 90.819. Also, the models exhibit lesser values for different Statistical Error Indicators (SEI) - MAE, RMSE, and MAPE