Improved pos tagging for text-to-speech synthesis

By Ming Sun, Jerome R. Bellegarda. 2011

13 citations recorded by the source corpus.

One of the fundamental building blocks of text processing for text-to-speech (TTS) synthesis is the assignment of a part-of-speech (POS) tag to each input word. POS tags are heavily relied upon for downstream natural language analysis and prosody rendering. Conventional TTS POS tagging tends to resort to detailed handcrafted rules that can accommodate TTS specificities such as pertinent prosodic features, while mainstream tagging increasingly relies on data-driven statistical models trained on large but fairly generic corpora. This paper proposes a new strategy, hybrid POS tagging, which integrates these two approaches in order to achieve higher tagging accuracy. The resulting framework combines the TTS-specific advantage of rule-based tagging with the inherent robustness of broadly-trained statistical tagging. Empirical evidence underscores the viability of this framework for improving TTS quality, e.g., in regard to phrase boundary placement and homograph selection.

Subfields: Artificial Intelligence

How researchers see this paper

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

In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Sheng Zhao
6. Yi Ren
7. Zhou Zhao
8. Lei He

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “Task Research Work Text Normalization Rule-based [311], Neural-based [310, 223, 406, 430], Hybrid [432] Word Segmentation [394, 444, 261] POS Tagging [292, 323, 221, 444, 135] Prosody Prediction [50, 405, 312, 186, 137, 322, 277, 62, 440, 210, 212, 3] Grapheme to Phoneme N-gram [41, 24], Neural-base”