Tibetan Word Segmentation as Syllable Tagging Using Conditional Random Field

In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He.In this paper, we proposed a novel approach for Tibetan word segmentation using the conditional random field. We reformulate the segmentation as a syllable tagging problem. The approach labels each syllable with a word-internal position tag, and combines syllable(s) into words according to their tags. As there is no public available Tibetan word segmentation corpus, the training corpus is generated by another segmenter which has an F-score of 96.94% on the test set. Two feature template sets namely TMPT-6 and TMPT-10 are used and compared, and the result shows that the former is better. Experiments also show that larger training set improves the performance significantly. Trained on a set of 131,903 sentences, the segmenter achieves an F-score of 95.12% on the test set of 1,000 sentences. © 2011 by Huidan Liu, Minghua Nuo, Longlong Ma, Jian Wu, and Yeping He

Detecting stuttering events in transcripts of children’s speech

Stuttering is a common problem in childhood that may persist into adulthood if not treated in early stages. Techniques from spoken language understanding may be applied to provide automated diagnosis of stuttering from children speech. The main challenges however lie in the lack of training data and the high dimensionality of this data. This study investigates the applicability of machine learning approaches for detecting stuttering events in transcripts. Two machine learning approaches were applied, namely HELM and CRF. The performance of these two approaches are compared, and the effect of data augmentation is examined in both approaches. Experimental results show that CRF outperforms HELM by 2.2% in the baseline experiments. Data augmentation helps improve systems performance, especially for rarely available events. In addition to the annotated augmented data, this study also adds annotated human transcriptions from real stuttered children’s speech to help expand the research in this field