What makes a word: Learning base units in Japanese for speech recognition

We describe an automatic process for learning word units in Japanese. Since the Japanese orthography has no spaces delimiting words, the first step in building a Japanese speech recognition system is to define the units that will be recognized. Our method applies a compound-finding algorithm, previously used to find word sequences in English, to learning syllable sequences in Japanese. We report that we were able not only to extract meaningful units, eliminating the need for possibly inconsistent manual segmentation, but also to decrease perplexity using this automatic procedure, which relies on a statistical, not syntactic, measure of relevance. Our algorithm also uncovers the kinds of environments that help the recognizer predict phonological alternations, which are often hidden by morphologically-motivated tokenization

Adaptation methods for non-native speech

LVCSR performance is consistently poor on low-proficiency non-native speech. While gains from speaker adaptation can often bring recognizer performance on highpro ciency non-native speakers close to that seen for native speakers [12], recognition for lower-proficiency speakers remains low even after individual speaker adaptation [2]. The challenge for accent adaptation is to maximize recognizer performance without collecting large amounts of acoustic data for each native-language/target-language pair. In this paper, we focus on adaptation for lower-proficiency speakers, exploring how acoustic data from up to 15 adaptation speakers can be put to its most effective use