二重分節分析と共起手がかりに基づく教師なしマルチモーダル単語発見
Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues
音韻分布と視覚・触覚・聴覚の共起情報を組み合わせ、教師なしで音素と単語を同時に獲得する手法を提案し、物体に対応する名詞・形容詞の分節精度が向上することを示した。
著者: Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki, Tadahiro Taniguchi
分類: cs.AI, cs.CL, cs.RO
原文アブストラクト
Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. This study proposes a novel fully unsupervised learning method for discovering speech units using phonological information as a distributional cue and object information as a co-occurrence cue. The proposed method can acquire words and phonemes from speech signals using unsupervised learning and utilize object information based on multiple modalities-vision, tactile, and auditory-simultaneously. The proposed method is based on the nonparametric Bayesian double articulation analyzer (NPB-DAA) discovering phonemes and words from phonological features, and multimodal latent Dirichlet allocation (MLDA) categorizing multimodal information obtained from objects. In an experiment, the proposed method showed higher word discovery performance than baseline methods. Words that expressed the characteristics of objects (i.e., words corresponding to nouns and adjectives) were segmented accurately. Furthermore, we examined how learning performance is affected by differences in the importance of linguistic information. Increasing the weight of the word modality further improved performance relative to that of the fixed condition.
