Unsupervised segmentation of words into morphemes - Morpho Challenge 2005, Application to Automatic Speech Recognition
Mikko Kurimo, Mathias Creutz, Matti Varjokallio, Ebru Arisoy and Murat Saraclar
In: Interspeech 2006, September 17-21, 2006, Pittsburgh, USA.
Within the EU Network of Excellence PASCAL,
a challenge was organized to design a
statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Ideally, these are basic vocabulary units suitable for different tasks, such as speech and text understanding, machine translation, information retrieval, and statistical language modeling.
Twelve research groups participated in the challenge and had submitted segmentation results obtained by their
In this paper, we evaluate the application of these segmentation algorithms to large vocabulary speech recognition using statistical n-gram language models based on the proposed word segments instead of entire words.
Experiments were done for two agglutinative and morphologically rich languages: Finnish and Turkish.
We also investigate combining various segmentations to improve the performance of the recognizer.