full transcript

From the Ted Talk by Rupal Patel: Synthetic voices, as unique as fingerprints

Unscramble the Blue Letters

Rupal Patel: That was the voice of Professor Stephen Hawking. What you may not know is that same voice may also be used by this little girl who is ubalne to speak because of a neurological condition. In fact, all of these individuals may be using the same voice, and that's because there's only a few options available. In the U.S. alone, there are 2.5 million Americans who are unable to speak, and many of whom use computerized devices to canomictume. Now that's mliionls of people worldwide who are using generic voices, including Professor Hawking, who uses an American-accented voice. This lack of iiiauniodvdtn of the syenhittc voice really hit home when I was at an atsisvsie technology conference a few years ago, and I recall walking into an exhibit hall and seeing a little girl and a grown man having a conversation using their devices, different dceievs, but the same voice. And I looked around and I saw this happening all around me, literally hundreds of individuals using a handful of voices, veicos that didn't fit their bodies or their personalities. We wouldn't dream of fitting a little girl with the prosthetic limb of a grown man. So why then the same pethoristc voice? It really struck me, and I watend to do something about this.

Open Cloze

Rupal Patel: That was the voice of Professor Stephen Hawking. What you may not know is that same voice may also be used by this little girl who is ______ to speak because of a neurological condition. In fact, all of these individuals may be using the same voice, and that's because there's only a few options available. In the U.S. alone, there are 2.5 million Americans who are unable to speak, and many of whom use computerized devices to ___________. Now that's ________ of people worldwide who are using generic voices, including Professor Hawking, who uses an American-accented voice. This lack of _____________ of the _________ voice really hit home when I was at an _________ technology conference a few years ago, and I recall walking into an exhibit hall and seeing a little girl and a grown man having a conversation using their devices, different _______, but the same voice. And I looked around and I saw this happening all around me, literally hundreds of individuals using a handful of voices, ______ that didn't fit their bodies or their personalities. We wouldn't dream of fitting a little girl with the prosthetic limb of a grown man. So why then the same __________ voice? It really struck me, and I ______ to do something about this.

Solution

  1. prosthetic
  2. wanted
  3. voices
  4. assistive
  5. synthetic
  6. millions
  7. unable
  8. devices
  9. communicate
  10. individuation

Original Text

Rupal Patel: That was the voice of Professor Stephen Hawking. What you may not know is that same voice may also be used by this little girl who is unable to speak because of a neurological condition. In fact, all of these individuals may be using the same voice, and that's because there's only a few options available. In the U.S. alone, there are 2.5 million Americans who are unable to speak, and many of whom use computerized devices to communicate. Now that's millions of people worldwide who are using generic voices, including Professor Hawking, who uses an American-accented voice. This lack of individuation of the synthetic voice really hit home when I was at an assistive technology conference a few years ago, and I recall walking into an exhibit hall and seeing a little girl and a grown man having a conversation using their devices, different devices, but the same voice. And I looked around and I saw this happening all around me, literally hundreds of individuals using a handful of voices, voices that didn't fit their bodies or their personalities. We wouldn't dream of fitting a little girl with the prosthetic limb of a grown man. So why then the same prosthetic voice? It really struck me, and I wanted to do something about this.

Frequently Occurring Word Combinations

ngrams of length 2

collocation frequency
unique vocal 3
grown man 2
severe speech 2
vocal identities 2
personalized voices 2
vocal identity 2
source characteristics 2
voice bank 2

ngrams of length 3

collocation frequency
unique vocal identities 2

Important Words

  1. americans
  2. assistive
  3. bodies
  4. communicate
  5. computerized
  6. condition
  7. conference
  8. conversation
  9. devices
  10. dream
  11. exhibit
  12. fact
  13. fit
  14. fitting
  15. generic
  16. girl
  17. grown
  18. hall
  19. handful
  20. happening
  21. hawking
  22. hit
  23. home
  24. hundreds
  25. including
  26. individuals
  27. individuation
  28. lack
  29. limb
  30. literally
  31. looked
  32. man
  33. million
  34. millions
  35. neurological
  36. options
  37. people
  38. personalities
  39. professor
  40. prosthetic
  41. recall
  42. rupal
  43. speak
  44. stephen
  45. struck
  46. synthetic
  47. technology
  48. unable
  49. voice
  50. voices
  51. walking
  52. wanted
  53. worldwide
  54. years