  Interactions based on automatic speech recognition (ASR) have become widely
used, with speech input being increasingly utilized to create documents.
However, as there is no easy way to distinguish between commands being issued
and text required to be input in speech, misrecognitions are difficult to
identify and correct, meaning that documents need to be manually edited and
corrected. The input of symbols and commands is also challenging because these
may be misrecognized as text letters. To address these problems, this study
proposes a speech interaction method called DualVoice, by which commands can be
input in a whispered voice and letters in a normal voice. The proposed method
does not require any specialized hardware other than a regular microphone,
enabling a complete hands-free interaction. The method can be used in a wide
range of situations where speech recognition is already available, ranging from
text input to mobile/wearable computing. Two neural networks were designed in
this study, one for discriminating normal speech from whispered speech, and the
second for recognizing whisper speech. A prototype of a text input system was
then developed to show how normal and whispered voice can be used in speech
text input. Other potential applications using DualVoice are also discussed.
