Neural Text Normalization with Subword Units

By Courtney Mansfield, Ming Sun, Ming Sun, Yuzong Liu, Ankur Gandhe, Björn Hoffmeister. 2019

42 citations recorded by the source corpus.

Text normalization (TN) is an important step in conversational systems. It converts written text to its spoken form to facilitate speech recognition, natural language understanding and text-to-speech synthesis. Finite state transducers (FSTs) are commonly used to build grammars that handle text normalization. However, translating linguistic knowledge into grammars requires extensive effort. In this paper, we frame TN as a machine translation task and tackle it with sequence-to-sequence (seq2seq) models. Previous research focuses on normalizing a word (or phrase) with the help of limited word-level context, while our approach directly normalizes full sentences. We find subword models with additional linguistic features yield the best performance (with a word error rate of 0.17%).

Subfields: Artificial Intelligence

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In Artificial Intelligence

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

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “TTS Text Analysis Char→Linguistic TN [310, 223, 430], G2P [403, 320, 321] Prosody Prediction [312, 137, 277, 251] Unified Model [252, 437] DeepVoice 1/2 [8, 84]”
• Seq2Edits: Sequence Transduction Using Span-level Edit Operations (2020, background): “5152 System SER↓ English Russian Mansfield et al. (2019)∗ 2.”
• A Hybrid Text Normalization System Using Multi-Head Self-Attention For Mandarin (2019, methodology): “Different seq2seq models with bi-LSTM, biGRU with attention are proposed in [10, 11].”