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In speech-related classification tasks, frequency-domain acoustic featuressuch as logarithmic Mel-filter bank coefficients (FBANK) and cepstral-domainacoustic features such as Mel-frequency cepstral coefficients (MFCC) are oftenused. However, time-domain features perform more effectively in some soundclassification 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 andshowed comparable accuracy performance in limited-vocabulary speech recognitiontasks. In this paper, we propose a novel improvement BSR feature calledBSR-float16 to represent floating-point values more precisely. Weexperimentally demonstrated the complementarity among time-domain,frequency-domain, and cepstral-domain features using a dataset called SpeechCommands proposed by Google. Therefore, we used a simple back-end score fusionmethod to improve the final classification accuracy. The fusion results alsoshowed better noise robustness.

Combination of Time-domain, Frequency-domain, and Cepstral-domain Acoustic Features for Speech Commands Classification