  With the ubiquity of mobile touchscreen devices like smartphones, two widely
used text entry methods have emerged: small touch-based keyboards and speech
recognition. Although speech recognition has been available on desktop
computers for years, it has continued to improve at a rapid pace, and it is
currently unknown how today's modern speech recognizers compare to
state-of-the-art mobile touch keyboards, which also have improved considerably
since their inception. To discover both methods' "upper-bound performance," we
evaluated them in English and Mandarin Chinese on an Apple iPhone 6 Plus in a
laboratory setting. Our experiment was carried out using Baidu's Deep Speech 2,
a deep learning-based speech recognition system, and the built-in Qwerty
(English) or Pinyin (Mandarin) Apple iOS keyboards. We found that with speech
recognition, the English input rate was 2.93 times faster (153 vs. 52 WPM), and
the Mandarin Chinese input rate was 2.87 times faster (123 vs. 43 WPM) than the
keyboard for short message transcription under laboratory conditions for both
methods. Furthermore, although speech made fewer errors during entry (5.30% vs.
11.22% corrected error rate), it left slightly more errors in the final
transcribed text (1.30% vs. 0.79% uncorrected error rate). Our results show
that comparatively, under ideal conditions for both methods, upper-bound speech
recognition performance has greatly improved compared to prior systems, and
might see greater uptake in the future, although further study is required to
quantify performance in non-laboratory settings for both methods.
