Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
By Aarne Talman, Antti Suni, H. Çelikkanat, Sofoklis Kakouros, Jörg Tiedemann, Martti Vainio. 2019
31 citations recorded by the source corpus.
In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest publicly available dataset with prosodic labels. We describe the dataset construction and the resulting benchmark dataset in detail and train a number of different models ranging from feature-based classifiers to neural network systems for the prediction of discretized prosodic prominence. We show that pre-trained contextualized word representations from BERT outperform the other models even with less than 10% of the training data. Finally we discuss the dataset in light of the results and point to future research and plans for further improving both the dataset and methods of predicting prosodic prominence from text. The dataset and the code for the models will be made publicly available.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- A Survey on Neural Speech Synthesis (2021, background): “Information Granularity Language/Speaker Level Multi-lingual/speaker TTS [438, 241, 38] Paragraph Level Long-form reading [11, 389, 370] Utterance Level Timbre/Prosody/Noise [303, 377, 139, 315, 202, 39] Word/Syllable Level Fine-grained information [319, 113, 44, 329] Character/Phoneme Level [318, 4”
- Unified Mandarin TTS Front-end Based on Distilled BERT Model (2020, methodology): “Inspired by the success of pre-trained language model (PLM) in TTS front-end related tasks [15, 16, 17, 18], we proposed a novel unified Mandarin TTS front-end model based on Chinese BERT, and further compressed it by utilizing a knowledge distillation technique named TinyBERT[2].”