Robust voice activity detection using cepstral features

In many speech signal processing applications, voice activity detection (VAD) plays an essential role for separating an audio stream into time intervals that contain speech activity and time intervals where speech is absent. Many features that reflect the presence of speech were introduced in literature. However, to our knowledge, no extensive comparison has been provided yet. In this article, we therefore present a structured overview of several established VAD features that target at different properties of speech. We categorize the features with respect to properties that are exploited, such as power, harmonicity, or modulation, and evaluate the performance of some dedicated features. The importance of temporal context is discussed in relation to latency restrictions imposed by different applications. Our analyses allow for selecting promising VAD features and finding a reasonable trade-off between performance and complexity.

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“…(16) separate the spectral envelope from the excitation. The spectral shape is characterized by the lower order coefficients [37].…”

Section: Formant Structurementioning

confidence: 99%

Features for voice activity detection: a comparative analysis

Graf

Herbig²,

Buck³

et al. 2015

EURASIP J. Adv. Signal Process.

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“…Some of the most used measurements are: energy, parameters of linear predictive coding [2], zero-crossing rate [3], periodicity [4], cepstral features [5], formant configurations [6], and spectral entropy [7]. Three VADs based on some of these techniques should be emphasized: G.729-B [16], G.729-II which is an improved version of the first [17], and the long-term spectral divergence (LTSD) [1].…”

Section: Introductionmentioning

confidence: 99%

Voice activity detection using discriminative restricted Boltzmann machines

Borin

Silva

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Voice Activity Detection (VAD) plays an important role in current technological applications, such as wireless communications and speech recognition. In this paper, we address the VAD task through machine learning by using a discriminative restricted Boltzmann machine (DRBM). We extend the conventional DRBM to deal with continuous-valued data and employ feature vectors based either on mel-frequency cepstral coefficients or on filter-bank energies. The resulting detector slightly outperforms the VAD often used as benchmark for detector comparison. Results also indicate that DRBM is able to deal with strongly correlated feature vectors.

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“…It has better detection capability and good adaptability and anti-noise ability in the case of low SNR [5]. Haigh and Mason proposed the method voice activity detection using cepstral features [6]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on Speech Endpoint Detection Algorithm with Low SNR

Wei¹,

Sun²

2017

OALib

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The detection of speech endpoint is an important application for speech signal processing. Although there are variance methods, the endpoint can't be detected accurately in low SNR (Signal to Noise Ratio). The paper pointes out an endpoint detection algorithm combining two methods together: the one is improved spectral subtraction based on multitaper spectral estimation, and the other is BARK subband variance in frequency domain. Firstly, the noisy speech signal is processed though the improved spectral subtraction based on multitaper spectral estimation. It can achieve the purpose of noise reduction through this step. Then the noisy speech signal is detected using the method of BARK subband variance in frequency domain. Compared with the common endpoint detection algorithm, it is concluded that endpoint detection accuracy by new method can be improved in low SNR.

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Robust voice activity detection using cepstral features

Cited by 145 publications

References 6 publications

Features for voice activity detection: a comparative analysis

Features for voice activity detection: a comparative analysis

Voice activity detection using discriminative restricted Boltzmann machines

Research on Speech Endpoint Detection Algorithm with Low SNR

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