Voice Activity Detection Technique Research Articles

Audio Mixer Algorithm for Enhancing Speech Quality of Multi-party Audio Telephony

The speech quality of multi-party audio telephony between two, three or more participants is decreased by audio volume imbalance, audio volume saturation and noise level increase. To solve this issue, this paper proposes an advanced audio mixing algorithm for software-based multi-point control unit. Our approach is based on the combined voice activity detection and gain control technique that consists of a set of algorithms that classify audio signals, estimate audio volumes, adjust gain factors and mix audio signals of all channels. The proposed audio mixing algorithm is computationally efficient, delivers high-quality speech, and is suitable for use in any practical multi-party audio telephony.

Multiple Acoustic Model-Based Discriminative Likelihood Ratio Weighting for Voice Activity Detection

In this letter, we propose a novel statistical voice activity detection (VAD) technique. The proposed technique employs probabilistically derived multiple acoustic models to effectively optimize the weights on frequency domain likelihood ratios with the discriminative training approach for more accurate voice activity detection. Experiments performed on various AURORA noisy environments showed that the proposed approach produces meaningful performance improvements over the single acoustic model-based conventional approaches.

Statistical Model-Based Voice Activity Detection Based on Second-Order Conditional MAP with Soft Decision

In this paper, we propose a novel approach to statistical model-based voice activity detection (VAD) that incorporates a second-order conditional maximum a posteriori (CMAP) criterion. As a technical improvement for the first-order CMAP criterion in [1], we consider both the current observation and the voice activity decision in the previous two frames to take full consideration of the interframe correlation of voice activity. This is clearly different from the previous approach [1] in that we employ the voice activity decisions in the second-order (previous two frames) CMAP, which has quadruple thresholds with an additional degree of freedom, rather than the first-order (previous single frame). Also, a soft-decision scheme is incorporated, resulting in time-varying thresholds for further performance improvement. Experimental results show that the proposed algorithm outperforms the conventional CMAP-based VAD technique under various experimental conditions.

Maximum Margin Clustering Based Statistical VAD With Multiple Observation Compound Feature

In this letter, we propose a new robust feature and an unsupervised learning approach for statistical voice activity detection (VAD). Maximum margin clustering (MMC), as an unsupervised classifier, can improve the robustness of support vector machine (SVM) based VAD while requiring no data labeling for model training. In the MMC framework, the multiple observation compound feature (MO-CF) is proposed to improve accuracy. MO-CF is composed of two subfeatures-multiple observation signal-to-noise ratio (MO-SNR) and multiple observation maximum probability (MO-MP). The contributions of the two subfeatures are balanced by a factor which is chosen to yield the largest area under the ROC curve (AUC) of the performance. The proposed approach obtains improved performance over seven commonly used VAD techniques in the experiments covering various noisy scenarios with low SNRs.

Enhanced voice activity detection using acoustic event detection and classification

We examine user-friendly voice interface that requires the hands-free speech acquisition in the continuously listening environment. The traditional voice activity detection (VAD) algorithms cannot successfully identify potential acoustic event sounds from speech. This makes the speech recognition system frequently or incorrectly activated. In this paper, we propose a novel voice activity detection technique that consists of two major modules: 1) classification and 2) detection module. In the classification module, we label the successive audio segments based on the training models. Then, in the detection module, we remove the acoustic event sounds and make decision of the explicit utterance boundary from the input audio stream. As a result, the proposed technique enables the efficient operation of speech recognition in the continuously listening environment without any touch and/or key input. Experiments in a real-world environment and performance comparison with state-of-the-art techniques are conducted to demonstrate the effectiveness of the proposed technique.

A Weighted Feature Voting Approach for Robust and Real-Time Voice Activity Detection

This paper concerns a robust real-time voice activity detection (VAD) approach which is easy to understand and implement. The proposed approach employs several short-term speech/nonspeech discriminating features in a voting paradigm to achieve a reliable performance in different environments. This paper mainly focuses on the performance improvement of a recently proposed approach which uses spectral peak valley difference (SPVD) as a feature for silence detection. The main issue of this paper is to apply a set of features with SPVD to improve the VAD robustness. The proposed approach uses a weighted voting scheme in order to take the discriminative power of the employed feature set into account. The experiments show that the proposed approach is more robust than the baseline approach from different points of view, including channel distortion and threshold selection. The proposed approach is also compared with some other VAD techniques for better confirmation of its achievements. Using the proposed weighted voting approach, the average VAD performance is increased to 89.29% for 5 different noise types and 8 SNR levels. The resulting performance is 13.79% higher than the approach based only on SPVD and even 2.25% higher than the not-weighted voting scheme.

Voice activity detection based on using wavelet packet

This paper, presents a robust voice activity detection (VAD) technique based on wavelet packet. In this technique sub-bands and their amplitudes are represented as the vectors for each sample time in order to find a new feature from the frequency and amplitude changes. On the other hand, the multi-resolution analysis property of the wavelet packet transform (WPT), the voiced, unvoiced, and transient components of speech can be distinctly discriminated. Then, a new feature extraction method is implemented based on observations of the angles between vectors. This feature extraction method retains most unvoiced sounds in a voice active frame. Experimental results show that the proposed WT feature parameter can extract the speech activity under poor SNR conditions and that it is also insensitive to variable-level of noise.

Noise robust voice activity detection based on periodic to aperiodic component ratio

This paper proposes a noise robust voice activity detection (VAD) technique called PARADE (PAR based Activity DEtection) that employs the periodic component to aperiodic component ratio (PAR). Conventional noise robust features for VAD are still sensitive to non-stationary noise, which yields variations in the signal-to-noise ratio, and sometimes requires a priori noise power estimations, although the characteristics of environmental noise change dynamically in the real world. To overcome this problem, we adopt the PAR, which is insensitive to both stationary and non-stationary noise, as an acoustic feature for VAD. By considering both periodic and aperiodic components simultaneously in the PAR, we can mitigate the effect of the non-stationarity of noise. PARADE first estimates the fundamental frequencies of the dominant periodic components of the observed signals, decomposes the power of the observed signals into the powers of its periodic and aperiodic components by taking account of the power of the aperiodic components at the frequencies where the periodic components exist, and calculates the PAR based on the decomposed powers. Then it detects the presence of target speech signals by estimating the voice activity likelihood defined in relation to the PAR. Comparisons of the VAD performance for noisy speech data confirmed that PARADE outperforms the conventional VAD algorithms even in the presence of non-stationary noise. In addition, PARADE is applied to a front-end processing technique for automatic speech recognition (ASR) that employs a robust feature extraction method called SPADE (Subband based Periodicity and Aperiodicity DEcomposition) as an application of PARADE. Comparisons of the ASR performance for noisy speech show that the SPADE front-end combined with PARADE achieves significantly higher word accuracies than those achieved by MFCC (Mel-frequency Cepstral Coefficient) based feature extraction, which is widely used for conventional ASR systems, the SPADE front-end without PARADE, and other standard noise robust front-end processing techniques (ETSI ES 202 050 and ETSI ES 202 212). This result confirmed that PARADE can improve the performance of front-end processing for ASR.