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Voice activity detection (VAD) is still a difficult task in noisy environments since the statistical distributions of speech and non-speech features are heavily overlapped in noisy environments. Considering that speech is a special type of acoustic signal that only occupies a small fraction of the whole acoustic space, we have proposed a new speech processing method for VAD by giving constraints on...
Traditional noise reduction methods usually are based on the assumption that the short-term statistical distributions of speech and noise are different. Differently from that assumption, we have proposed a noise reduction method based on the assumption that the temporal modulations of noise and speech are different. Two steps are used in the proposed algorithm: one is the temporal modulation contrast...
In this paper, we propose a two-step processing algorithm which adaptively normalizes the temporal modulation of speech to extract robust speech feature for automatic speech recognition systems. The first step processing is to normalize the temporal modulation contrast (TMC) of the cepstral time series for both clean and noisy speech. The second step processing is to smooth the normalized temporal...
We study a single-shot single-flux-quantum (SFQ) readout circuit based on current-to-time conversion for superconducting flux qubits. Circulating currents representing states of a qubit were converted to propagation delay time of SFQ pulses, and then detected by a time discriminator. The key parameter which determines the availability of single-shot readout is current resolution. We evaluated the...
In speech enhancement literature, the signal subspace based method gains a lot of attention because of its simplicity in analytical formulations. The original idea in this method is based on the assumption that clean speech signal occupies a certain low dimensional space, while the noise signal which is a white additive noise spread the whole observation space. In this method, accurate estimation...
In this paper, we proposed a robust speech feature extraction algorithm for automatic speech recognition which reduced the noise effect in the temporal modulation domain. The proposed algorithm has two steps to deal with the time series of cepstral coefficients. The first step adopted a modulation contrast normalization to normalize the temporal modulation contrast of both clean and noisy speech to...
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