  In speech-related classification tasks, frequency-domain acoustic features
such as logarithmic Mel-filter bank coefficients (FBANK) and cepstral-domain
acoustic features such as Mel-frequency cepstral coefficients (MFCC) are often
used. However, time-domain features perform more effectively in some sound
classification tasks which contain non-vocal or weakly speech-related sounds.
We previously proposed a feature called bit sequence representation (BSR),
which is a time-domain binary acoustic feature based on the raw waveform.
Compared with MFCC, BSR performed better in environmental sound detection and
showed comparable accuracy performance in limited-vocabulary speech recognition
tasks. In this paper, we propose a novel improvement BSR feature called
BSR-float16 to represent floating-point values more precisely. We
experimentally demonstrated the complementarity among time-domain,
frequency-domain, and cepstral-domain features using a dataset called Speech
Commands proposed by Google. Therefore, we used a simple back-end score fusion
method to improve the final classification accuracy. The fusion results also
showed better noise robustness.
