Feature For Voice Activity Detection Research Articles

A Novel Modified Mel-DCT Filter Bank Structure With Application to Voice Activity Detection

We propose a novel modified Mel-discrete cosine transform (MMD) filter bank structure, which restricts the overlap of each filter response to its immediate neighbor. In contrast to the well-known triangular filters employed in the extraction of the Mel-frequency cepstral coefficients (MFCC), the proposed filter structure has a smoother response and offers discrete cosine transformation and Mel-scale filtering in a single operation. It is known that the choice of MFCC as the only feature for voice activity detection (VAD) does not yield substantial improvements in the performance. Even with the long-term approach, we observe a not so encouraging VAD performance when MFCC features are employed. However, other long-term based VAD algorithms – without MFCC – are known to provide a substantial improvement in the performance under low SNR with time-varying statistics of speech and/or noise. In this work, we show that by employing the MMD followed by the long-term differential entropy of voice signal for VAD provides significant improvements in detection accuracy when compared with the other well-known long-term algorithms. Thus, this study opens up the possible benefits of the proposed MMD filter bank for other speech processing applications.

Formant-Based Robust Voice Activity Detection

Voice activity detection (VAD) can be used to distinguish human speech from other sounds, and various applications can benefit from VAD-including speech coding and speech recognition. To accurately detect voice activity, the algorithm must take into account the characteristic features of human speech and/or background noise. In many real-life applications, noise frequently occurs in an unexpected manner, and in such situations, it is difficult to determine the characteristics of noise with sufficient accuracy. As a result, robust VAD algorithms that depend less on making correct noise estimates are desirable for real-life applications. Formants are the major spectral peaks of the human voice, and these are highly useful to distinguish vowel sounds. The characteristics of the spectral peaks are such that, these peaks are likely to survive in a signal after severe corruption by noise, and so formants are attractive features for voice activity detection under low signal-to-noise ratio (SNR) conditions. However, it is difficult to accurately extract formants from noisy signals when background noise introduces unrelated spectral peaks. Therefore, this paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise. The proposed method achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions. The proposed method is robust against various types of noise and produces a light computational load, so it is suitable for use in various applications.

Features for voice activity detection: a comparative analysis

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.

비정체성 잡음을 위한 SPD-TE 기반 계수형 음성 활동 탐지

A single channel VAD (Voice Activity Detection) algorithm for nonstationary noise environment is proposed in this paper. Threshold values of the feature parameter for VAD decision are updated adaptively based on estimates of means and standard deviations of past non-speech frames. The feature parameter, SPD-TE (Spectral Power Difference-Teager Energy), is obtained by applying the Teager energy to the WPD (Wavelet Packet Decomposition) coefficients. It was reported previously that the SPD-TE is robust to noise as a feature for VAD. Experimental results by using TIMIT speech and NOISEX-92 noise databases show that decision accuracy of the proposed algorithm is comparable to several typical VAD algorithms including standards for SNR values ranging from 10 to -10 dB.

Robust Voice Activity Detection Using Long-Term Signal Variability

We propose a novel long-term signal variability (LTSV) measure, which describes the degree of nonstationarity of the signal. We analyze the LTSV measure both analytically and empirically for speech and various stationary and nonstationary noises. Based on the analysis, we find that the LTSV measure can be used to discriminate noise from noisy speech signal and, hence, can be used as a potential feature for voice activity detection (VAD). We describe an LTSV-based VAD scheme and evaluate its performance under eleven types of noises and five types of signal-to-noise ratio (SNR) conditions. Comparison with standard VAD schemes demonstrates that the accuracy of the LTSV-based VAD scheme averaged over all noises and all SNRs is ~6% (absolute) better than that obtained by the best among the considered VAD schemes, namely AMR-VAD2. We also find that, at -10 dB SNR, the accuracies of VAD obtained by the proposed LTSV-based scheme and the best considered VAD scheme are 88.49% and 79.30%, respectively. This improvement in the VAD accuracy indicates the robustness of the LTSV feature for VAD at low SNR condition for most of the noises considered.

Long-Term Spectro-Temporal and Static Harmonic Features for Voice Activity Detection

Accurate voice activity detection (VAD) is important for robust automatic speech recognition (ASR) systems. This paper proposes a statistical-model-based noise-robust VAD algorithm using long-term temporal information and harmonic-structure-based features in speech. Long-term temporal information has recently become an ASR focus, but has not yet been deeply investigated for VAD. In this paper, we first consider the temporal features in a cepstral domain calculated over the average phoneme duration. In contrast, the harmonic structures are well-known bearers of acoustic information in human voices, but that information is difficult to exploit statistically. This paper further describes a new method to exploit the harmonic structure information with statistical models, providing additional noise robustness. The proposed method including both the long-term temporal and the static harmonic features led to considerable improvements under low SNR conditions, with 77.7% error reduction on average as compared with the ETSI AFE-VAD in our VAD testing. In addition, the word error rate was reduced by 29.1% in a test that included a full ASR system.

A speech feature extraction method using complexity measure for voice activity detection in WGN

A novel speech extraction algorithm is proposed in this paper for Voice Activity Detection (VAD). Signal complexity analysis with definition of Kolmogorov complexity is adopted, which explores model characteristics of speech production to differentiate speech and white Gaussian noise (WGN). In the view of speech signal processing, properties of speech’s source and vocal tract are explored by complexity analysis. Also, some interesting properties of signal complexity are presented with experimental study, including complexity analysis of general noise-corrupted signal. Moreover, some enhanced features with complexity and a feature incorporation method are presented. These features incorporate some unique characteristics of speech, like pitch information, vocal organ information, and so on. With a large database of speech signals and synthetic/real Gaussian noise, distributions of novel features and receiver operating characteristics (ROC) curves are shown, which are proved as potential features for voice activity detection.

音声と画像の統合によるドライバの発話区間検出

Voice activity detection is an important part of the development of speech functions for on-board car navigation and assistance systems. It is difficult to detect voice activity using only sound information in a vehicle environment that has a wide variety of sounds and noises. We propose an suitable image feature and integration method that can be used to develop a robust bimodal voice activity detection (VAD) systems using a driver's voice and facial images. We select the normal correlation value between sequential mouth images and the number of low-intensity pixels in mouth image, which we then used as the feature for VAD. We propose a system in which the discrimination function consist of the sum of weighted singles feature discrimination functions and combinations of logical addition and multiplication of singles feature discrimination functions. The experimental results show that the proposed sound and image features can be useful and that the proposed integration method has a 97% hit rate, which is 9 points better than the previous integration method at the point that false alarm rate is about 12%.