Automatic Prosodic Labeling with Conditional Random Fields and Rich Acoustic Features

By Gina-Anne Levow. 2008

31 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Aarne Talman
5. Sofoklis Kakouros
6. Lei Xie
7. Jörg Tiedemann
8. Martti Vainio

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “A lot of works [312, 186, 137, 277] investigate different models and features to predict the prosody tags based on ToBI.”
• Automatic prosody prediction for Chinese speech synthesis using BLSTM-RNN and embedding features (2015, background): “Index Terms— automatic prosody prediction, speech synthesis, neural network, BLSTM, embedding features”
• Self-attention Based Prosodic Boundary Prediction for Chinese Speech Synthesis (2019, methodology): “BLSTM-CRF: BLSTM-CRF based model which uses word embeddings as input and achieves state-of-the-art results in [11] is used as our baseline.”
• Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations (2019, methodology): “, 2001), and with the probabilities of contiguous lexical sequences (Jurafsky, 1996), automatic methods have been developed utilizing these features either in combination or independently (Nenkova et al., 2007; Kakouros et al., 2016; Ostendorf et al., 1995; Levow, 2008).”