著者
中村 友昭 長井 隆行 船越 孝太郎 谷口 忠大 岩橋 直人 金子 正秀
出版者
一般社団法人 人工知能学会
雑誌
人工知能学会論文誌 (ISSN:13460714)
巻号頁・発行日
vol.30, no.3, pp.498-509, 2015-05-01 (Released:2015-03-26)
参考文献数
30
被引用文献数
1

Humans develop their concept of an object by classifying it into a category, and acquire language by interacting with others at the same time. Thus, the meaning of a word can be learnt by connecting the recognized word and concept. We consider such an ability to be important in allowing robots to flexibly develop their knowledge of language and concepts. Accordingly, we propose a method that enables robots to acquire such knowledge. The object concept is formed by classifying multimodal information acquired from objects, and the language model is acquired from human speech describing object features. We propose a stochastic model of language and concepts, and knowledge is learnt by estimating the model parameters. The important point is that language and concepts are interdependent. There is a high probability that the same words will be uttered to objects in the same category. Similarly, objects to which the same words are uttered are highly likely to have the same features. Using this relation, the accuracy of both speech recognition and object classification can be improved by the proposed method. However, it is difficult to directly estimate the parameters of the proposed model, because there are many parameters that are required. Therefore, we approximate the proposed model, and estimate its parameters using a nested Pitman--Yor language model and multimodal latent Dirichlet allocation to acquire the language and concept, respectively. The experimental results show that the accuracy of speech recognition and object classification is improved by the proposed method.

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https://t.co/1GHQDtSEZ9 マルチモーダルLDAとNPLYMを用いたロボットによる物体概念と言語モデルの相互学習 これ楽しそうやりたい(小並感)
日本語母音のみで構成された発話からの単語境界の推定は随分とできたのだが、子音が豊かに入るときついし、抑揚が強くなってもまあキツイ。(2)分布情報だけじゃ情報が多分足りない。(3)の状況との共起に関しては 中村友昭 2014 https://t.co/4B9mdQDGGz で音節獲得後を仮定したモデルは出してる
publishされました。和文ですが中々面白い内容のハズなので御一読たまわれれば、幸いです!>>マルチモーダルLDAとNPYLMを用いたロボットによる物体概念と言語モデルの相互学習 https://t.co/4B9mdQDGGz

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