英語 IA 1A5 (=E1R86), 1L1 (=E1R05) , 英語 IIA E2R40 , 2011 第 7 回 ( 全 10 回 ) 黒田 航 ( 非常勤 ) 出口雅也 ( 非常勤 ) の代理 2011-06-30 Wednesday, July 6, 2011
で，自習に使って良いです 使ったスライドはこのページから入手可能 講義資料の Web ページ ✤ URL ✤ http://clsl.hi.h.kyoto-u.ac.jp/~kkuroda/lectures.html ✤ The Feynman Lectures on Physics の音源ファイルや授業で ✤ 予習や復習に使って下さい ✤ 解答もこのページから入手可能 ✤ 京都工芸繊維大学で使っている教材（過去の分）もあるの Wednesday, July 6, 2011
期末ボーナス試験 ✤ 7/28 ( 木 ) に試験をします ✤ 試験をしつつ， 4 回分の補講をするのは無理 ✤ 補講は期間外にはできないそうです ✤ この試験は任意参加のボーナス試験です ✤ 授業でやったのと同じ課題を行なう ✤ ハズレがアタリに ✤ アタリはアタリのまま Wednesday, July 6, 2011
本日の予定 ✤ 前半 30 分 1. L5 の聞き取り課題の結果の報告 2. 正解の解説 ✤ 休憩 5 分 ✤ 後半 40 分 ❖ 聞き取り訓練 L6 Wednesday, July 6, 2011
任意参加ではない方々 ✤ 1A5 ✤ 脇田 健史 ✤ 2R ✤ 大塚 直通 , 財前 雄太 , 乗竹 剛志 , 栗原 拓也 , 浦 順貴 , 大月 亮太 , 大野 遼 , 長谷川 栄貴 , 小野原 龍一 , 松井 孝憲 , 三野 春樹 , 福地 崇洋 , 小嶋 和也 , 原 拓矢 ✤ 1L1 ✤ 宮本 貴史 , 松元 大周 , 川崎 眞理子 , 原 祐太 , 窪田 かすみ Wednesday, July 6, 2011
L5 の結果 ( Deb Roy: The birth of a word, Part 2) Wednesday, July 6, 2011
L5 の得点分布 1A5, 2R, 1L1 ✤ 参加者 : 67 人 ✤ 平均 : 71.55; 標準偏差 : 10.48 ✤ 最高 : 90.83; 最低 : 43.33 ✤ 得点グループ ✤ 80 点が中心のグループ Wednesday, July 6, 2011
プ L5 の得点分布 1A5 ✤ 受講者数 : 23 ✤ 平均 : 42.83/ n [71.38] 点 ✤ 標準偏差 : 6.26/ n [ 9.62] 点 ✤ 最高 : 54.50/ n [90.83] 点 ✤ 最低 : 32.50/ n [54.17] 点 ✤ n = 60 ✤ 得点グループ ✤ 65 点 , 75 点 , 85 点 , 95 点が中心のグルー Wednesday, July 6, 2011
L5 の得点分布 2R ✤ 受講者数 : 16 ✤ 平均 : 40.13/ n [66.88] 点 ✤ 標準偏差 : 7.56/ n [12.59] 点 ✤ 最高 : 53.00/ n [88.33] 点 ✤ 最低 : 26.00/ n [43.33] 点 ✤ n = 60 ✤ 得点グループ ✤ 55 点 , 75 点が中心のグループ Wednesday, July 6, 2011
L5 の得点分布 1L1 ✤ 受講者数 : 28 ✤ 平均 : 44.63/ n [74.38] 点 ✤ 標準偏差 : 5.49/ n [ 9.15] 点 ✤ 最高 : 54.00/ n [90.00] 点 ✤ 最低 : 30.50/ n [50.83] 点 ✤ n = 60 ✤ 得点グループ ✤ 75 点 , 80 点 , 95 点が中心のグループ Wednesday, July 6, 2011
得点の変遷 (L5 まで ) Wednesday, July 6, 2011
L5 の正解率分布 1A5, 2R, 1L1 ✤ 参加者 : 67 人 ✤ 平均値 : 0.88 ✤ 最高値 : 0.95; 最低値 : 0.50 ✤ 標準偏差 : 0.07 ✤ 正答率のグループ ✤ 0.8 辺りが中心のグループ Wednesday, July 6, 2011
L5 の正答率分布 1A5 ✤ 参加者 : 23 人 ✤ 平均 : 0.85; 標準偏差 : 0.04 ✤ 最高 : 0.92; 最低 : 0.77 ✤ 正答率のグループ ✤ 0.9 が中心のグループ Wednesday, July 6, 2011
L5 の正答率分布 2R ✤ 参加者 : 16 人 ✤ 平均 : 0.84; 標準偏差 : 0.06 ✤ 最高 : 0.94; 最低 : 0.73 ✤ 正答率のグループ ✤ 0.9 が中心 Wednesday, July 6, 2011
L5 の正答率分布 1L1 ✤ 参加者 : 28 人 ✤ 平均 : 0.84; 標準偏差 : 0.05 ✤ 最高 : 0.93; 最低 : 0.70 ✤ 正答率のグループ ✤ 0.85 が中心 Wednesday, July 6, 2011
正答率の変遷 (L5 まで ) Wednesday, July 6, 2011
全体の評価 ✤ 1A5, 2R, 1L1 の全クラスで ✤ 得点と正答率のいずれでも， ✤ 2 番目に高い成績 ✤ 得点に関しては ✤ 1L1 が堅調 Wednesday, July 6, 2011
L5 の解答 (Deb Roy: The birth of a word ) Wednesday, July 6, 2011
誤りの傾向 1. at 17. landscape ⇒ 31. gives ⇒ keeps 46. pulse ⇒ pose, ports ✤ ✤ ✤ ✤ wordscape 2. welcome ⇒ walking, 32. a ⇒ eight, 8 47. collect ⇒ correct ✤ ✤ ✤ open 18. peering ⇒ pearing, ✤ 33. lives ⇒ leaves, was 48. dynamics ⇒ ✤ ✤ appearing 3. did ⇒ get dinamics, dyinamics, ✤ 34. third ⇒ three ✤ 19. people dynamix ✤ 4. me ⇒ mean ✤ 35. rendered ⇒ ✤ 20. following ⇒ phone, 50. profound ⇒ found ✤ ✤ 5. leaving ⇒ living around, learning, ✤ form landing 51. reflect ⇒ flight, ✤ 6. freeze ⇒ free ✤ 21. take fight ✤ 36. that ✤ 7. see ✤ 22. turn 52. guys ⇒ gaze, ✤ ✤ 37. if ⇒ for, free ✤ 8. call ⇒ go guides ✤ 23. same ✤ 38. that ⇒ at, not ✤ 9. when 53. gonna ✤ 24. satellite ⇒ all ✤ ✤ 39. living ✤ 10. here’s 54. powerful ✤ 25. feeds ⇒ series ✤ ✤ 40. into 11. off ⇒ often, ask ✤ 55. take ✤ ✤ 26. magic ✤ 41. finding ✤ 12. power 56. encouraging ⇒ ✤ 27. looking ✤ ✤ 42. fan-out ⇒ final encourage, in- ✤ 13. it ✤ 28. except ✤ 43. link 57. realizes ⇒ low 14. with ✤ ✤ ✤ 29. are ⇒ relate ✤ 44. address ⇒ brass, 58. kicks ⇒ keeps ✤ 15. wordscape ⇒ ✤ ✤ 30. sphere ⇒ grass, adress ✤ wordscapes 59. back experience, experiment ✤ 45. remarkable 16. for ✤ 60. walking ⇒ walking ✤ ✤ Wednesday, July 6, 2011
01/12 ✤ But that’s looking [ 1. at ] the speech context. What about the visual context? We’re now looking at— think of this as a dollhouse cutaway of, of our house. We’ve taken those circular fish-eye lens cameras, and we've done some optical correction, and then we can bring it into three-dimensional life. So [ 2. welcome ] to my home. This is a moment, one moment captured across multiple cameras. The reason we [ 3. did ] this is to create the ultimate memory machine, where you can go back and interactively fly around and then breathe video life into this system. What I’m going to do is give you an accelerated view of 30 minutes, again, of just life in the living room. That’s [ 4. me ] and my son on the floor. And there’s video analytics that are tracking our movements. My son is [ 5. leaving ] red ink, I am leaving green ink. We’re now on the couch, looking out through the window at cars passing by. And finally, my son playing in a walking toy by himself. Wednesday, July 6, 2011
02/12 ✤ Now we [ 6. freeze ] the action, 30 minutes, we turn time into the vertical axis, and we open up for a view of these interaction traces we’ve just left behind. And we [ 7. see ] these amazing structures— these little knots of two colors of thread, we call social hot spots . The spiral thread, we [ 8. call ] a solo hot spot . And we think that these affect the way language is learned. ✤ What we’d like to do is start understanding the interaction between these patterns and the language that my son is exposed to to see if we can predict how the structure of when words are heard affects [ 9. when ] they’re learned —so in other words, the relationship between words and what they’re about in the world. ✤ So [ 10. here’s ] how we’re approaching this. In this video, again, my son is being traced out. He’s leaving red ink behind. And there’s our nanny by the door. Wednesday, July 6, 2011
03/12 ✤ Nanny: You want water? ✤ Baby: Aaaa.) ✤ Nanny: All right. ✤ (Baby: Aaaa.) ✤ She offers water, and [ 11. off ] go the two worms over to the kitchen to get water. And what we’ve done is use the word “water” to tag that moment, that bit of activity. And now we take the [ 12. power ] of data and take every time my son ever heard the word “water” and the context he saw [ 13. it ] in, and we use it to penetrate through the video and find every activity trace that co-occurred [ 14. with ] an instance of water. And what this data leaves in its wake is a landscape. We call these wordscapes . This is the [ 15. wordscape ] for the word water, and you can see most of the action is in the kitchen. That’s where those big peaks are over to the left. Wednesday, July 6, 2011
04/12 ✤ And just [ 16. for ] contrast, we can do this with any word. We can take the word “bye” as in “good bye.” And we’re now zoomed in over the entrance to the house. And we look, and we find, as you would expect, a contrast in the [ 17. landscape ] where the word “bye” occurs much more in a structured way. So we’re using these structures to start predicting the order of language acquisition, and, and that’s ongoing work now. ✤ In my lab, which we’re [ 18. peering ] into now, at MIT —this is at the media lab. This has become my favorite way of video graphing just about any space. Three of the key [ 19. people ] in this project, Philip DeCamp, Rony Kubat and Brandon Roy are pictured here. Philip has been a close collaborator on all the visualizations you’re seeing. And Michael Fleischman was another Ph.D. student in my lab who worked with me on this home video analysis, and he made the [ 20. following ] observation: Wednesday, July 6, 2011
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