Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data
This addresses the need for better segmentation in low-resource settings, though it is incremental by building on existing speech-text integration ideas.
The paper tackles the problem of Chinese word segmentation by mining word boundaries from parallel speech/text data, achieving significant performance improvements in cross-domain and low-resource scenarios.
Inspired by early research on exploring naturally annotated data for Chinese word segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to mine word boundaries from parallel speech/text data. First we collect parallel speech/text data from two Internet sources that are related with CWS data used in our experiments. Then, we obtain character-level alignments and design simple heuristic rules for determining word boundaries according to pause duration between adjacent characters. Finally, we present an effective complete-then-train strategy that can better utilize extra naturally annotated data for model training. Experiments demonstrate our approach can significantly boost CWS performance in both cross-domain and low-resource scenarios.