Ishimitsu S.

In recent years, speech recognition systems have been introduced in a wide variety of environments such as vehicle instrumentation. Speech recognition plays an important role in ships' chief engineer systems. In such a system, speech recognition supports engine room controls, and lower than 0-dB signal-to-noise ratio (SNR) operability is required. In such a low SNR environment, a noise signal can be misjudged as speech, dramatically decreasing the recognition rate. Therefore, this study focuses on a recognition system that uses body-conducted signals. Such signals are seldom affected by background noise, and thus a high recognition rate can be expected in low SNR environments. However, within the construction of such systems, in order to create models specialized for body-conducted speech, learning data consisting of sentences that must be read in numerous times is required. Therefore, in the present study we applied a method in which the specific nature of body-conducted speech is reflected within an existing speech recognition system with only small numbers of vocalizations.