Research of Voice Activity Detection Algorithm

In this paper the quick and efficient definition of hangbefore and hangover criteria for Voice Activity Detection (VAD) algorithm is presented. Hangbefore and hangover criteria are integrated into VAD algorithm after basic VAD decision step. Using the hangbefore criterion, the problem of incorrect detection of unvoiced speech that occurs at the beginning and in the middle of the speech segment, can be solved. Using the hangover criterion, the problem of incorrect detection of unvoiced speech that occurs at the end and in the middle of the speech segment, can be improved. There is always a dilemma of how many frames should be taken for hangover and hangbefore criteria. The main purpose of this study was to set up the procedure, which would quickly and reliably define frames for hangbefore and hangover criteria. Our test results showed that the new quick procedure has led us to very similar final results as obtained with the previously known procedures.

A robust algorithm for voice activity detection (VAD) is presented. It defines a likelihood ratio test (LRT) involving multiple and independent observations of the bispectra. The proposed VAD provides significant improvements in speech/pause discrimination when compared to standardised and recently reported VADs.

Analysis of derivative of instantaneous frequency and its application to voice activity detection

Speech can be modeled as short bursts of vocal energy separated by silence gaps. During typical conversation talk-spurts comprise only 31.5% of each party's speech and the remaining 68.5% is silence. Communication systems can achieve significant gains in spectral efficiency and energy efficiency by disconnecting the users from the spectral resource during the silence periods. This paper develops a simple, efficient, and robust voice activity detection (VAD) algorithm to work in a mobile or portable environment exhibiting dynamically varying background noise. The VAD uses probabilistic distances based on the energy content, the periodicity, the stationarity and the spectral distribution within the low frequency band to decide if the presented speech frame is speech or silence.

This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90.11% and 91.75% under -5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms.

In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter that, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and nonspeech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.

This paper explores the fourth order cumulants (FOC) of the LPC residual of speech signals and presents a new algorithm for Voice Activity detection (VAD) based on the newly established FOC properties. Analytical expressions for the horizontal slice of the 4th cumulant as well as the kurtosis of voiced speech are derived based on a reported sinusoidal model [4]. The derivations demonstrate that the kurtosis of voiced speech is distinct from that of Gaussian noise and can be used to aid in detecting voicing. The proposed VAD combines FOC metrics with SNR measures to classify speech and noise frames. Its performance is compared to the ITU-T G.729B VAD [1] in various noise conditions, and quantified using the probability of correct and false classifications. The results show the proposed VAD has overall comparable performance to the G.729B: Its probability of false classification is lower in low SNR and Gaussian-like noise, but higher in speech-like noises.

In order to develop a novel voice sensor to detect human voices, the use of features which are more robust to noise is an important issue. Voice sensor is also called voice activity detection (VAD). Due to that the inherent nature of the formant structure only occurred on the speech spectrogram (well-known as voiceprint), Wu et al. were the first to use band-spectral entropy (BSE) to describe the characteristics of voiceprints. However, the performance of VAD based on BSE feature was degraded in colored noise (or voiceprint-like noise) environments. In order to solve this problem, we propose the two-dimensional part-band energy entropy (TD-PBEE) parameter based on two variables: part-band partition number upon frequency index and long-term window size upon time index to further improve the BSE-based VAD algorithm. The two variables can efficiently represent the characteristics of voiceprints on each critical frequency band and use long-term information for noisy speech spectrograms, respectively. The TD-PBEE parameter can be regarded as a PBEE parameter over time. First, the strength of voiceprints can be partly enhanced by using four entropies applied to four part-bands. We can use the four part-band energy entropies for describing the voiceprints in detail. Due to the characteristics of non-stationary for speech and various noises, we will then use long-term information processing to refine the PBEE, so the voice-like noise can be distinguished from noisy speech through the concept of PBEE with long-term information. Our experiments show that the proposed feature extraction with the TD-PBEE parameter is quite insensitive to background noise. The proposed TD-PBEE-based VAD algorithm is evaluated for four types of noises and five signal-to-noise ratio (SNR) levels. We find that the accuracy of the proposed TD-PBEE-based VAD algorithm averaged over all noises and all SNR levels is better than that of other considered VAD algorithms.

A new voice activity detection (VAD) algorithm with soft decision output in Mel-frequency domain is developed based on hidden Markov model (HMM) and is incorporated in an HMM-based speech enhancement system. The proposed VAD uses a two-state ergodic HMM representing speech presence and speech absence. The states are constructed from noisy speech and noise HMMs used in the speech enhancement system. This composite model provides a robust detection of speech segments in the presence of noise and obviates the need for extra modeling in HMM-based speech enhancement applications. As the main purpose of the proposed VAD is to detect speech segments accurately, a hang-over mechanism is proposed and is applied on the output of the VAD to improve the speech detection rate. The VAD is integrated in the HMM-based speech enhancement system in Mel-frequency spectral (MFS) and cepstral (MFC) domains. The performance of the proposed VAD, the effectiveness of the hang-over mechanism and the performance of the VAD-integrated speech enhancement system are evaluated on four noise types at different SNR levels. The experimental results confirm the superiority of the proposed VAD compared to the reference methods particularly for speech detection rate at the dominant noisy conditions.

We introduce an efficient hidden Markov model-based voice activity detection (VAD) algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multi-rate VAD, option 2 (AMR2).

This paper describes a new algorithm for voice activity detection (VAD), based on sparse representation of spectro-temporal domain. Our audio classification algorithm is based on multi-scale spectro-temporal modulation features which are extracted using auditory cortex model. The key concept in sparse representation is that any speech fragment can be represented as a linear combination of a small number of exemplar speech tokens. In this algorithm, the approach transforms the speech into spectro-temporal domain resulting in its decomposition into auditory-based features with multiple scales of temporal and spectral resolutions; in the next stage, each frame is divided into several sub-cubes in the new domain; then the algorithm detects the speech in the signal by using the sparse representation of sub-cubes of the frames in this domain. Simulation results are given to illustrate the effectiveness of our new VAD algorithms. The results reveal that the achieved performance is 90.11 and 91.75% under − 5 db SNR in white and car noise respectively, outperforming most of the state of the art VAD algorithms.

An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The proposed speech/pause discrimination method is based on a hard-decision clustering approach built over a set of subband log-energies. Detecting the presence of speech frames (a new cluster) is achieved using a basic sequential algorithm scheme (BSAS) according to a given “distance” (in this case, geometrical distance) and a suitable threshold. The accuracy of the Cl-VAD algorithm lies in the use of a decision function defined over a multiple-observation (MO) window of averaged subband log-energies and the modeling of noise subspace into cluster prototypes. In addition, time efficiency is also reached due to the clustering approach which is fundamental in VAD real time applications, i.e. speech recognition. An exhaustive analysis on the Spanish SpeechDat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms.

We present an approach to model-based voice activity detection (VAD) for harsh environments. By using mel-frequency cepstral coefficients feature extracted from clean and noisy speech samples, an artificial neural network is trained optimally in order to provide a reliable model. There are three main aspects to this study: First, in addition to the developed model, recent state-of-the-art VAD methods are analyzed extensively. Second, we present an optimization procedure of neural network training, including evaluation of trained network performance with proper measures. Third, a large assortment of empirical results on the noisy TIMIT and SNOW corpuses including different types of noise at different signal-to-noise ratios is provided. We evaluate the built VAD model on the noisy corpuses and compare against the state-of-the-art VAD methods such as the ITU-T Rec. G. 729 Annex B, the ETSI AFE ES 202 050, and recently promising VAD algorithms. Results show that: (i) the proposed neural network classifier employing MFCC feature provides robustly high scores under different noisy conditions; (ii) the invented model is superior to other VAD methods in terms of various classification measures; (iii) the robustness of the developed VAD algorithm is still hold in the case of testing it with the completely mismatched environment.

This paper presents an improved Voice Activity Detection (VAD) algorithm which uses the Signal-to-Noise Ratio (SNR) measure. We assume that noise Power Spectral Density (PSD) in each spectral bin follows a Rayleigh distribution. Rayleigh distributions with its asymmetric tail characteristics give a better description of the noise PSD distribution than Gaussian distribution. Under this assumption, a new threshold updating expression is derived. Since the analytical integral of the false alarm probability, the threshold updating expression can be represented without the inverse complementary error function and low computational complexity is achieved in our system. Experimental results show that the proposed VAD outperforms or at least is comparable with the VAD scheme presented by Davis under several noise environments and has a lower computational complexity.

This paper presents a low computational complexity and high robust voice activity detection (VAD) algorithm. The algorithm is based on an efficient time-domain pitch detection and harmonic structure discrimination exploiting frequency decomposition by 1/3 octave filter bank defined in ANSI S1.11 standard. In addition, a simple yet efficient phoneme keeper (PK) is adopted for the detection of monosyllable languages, such as Mandarin. Simulation results reveal that the proposed VAD has very robust performance for Mandarin speeches in different environments, even for dynamic SNR (signal to noise ratio) and noise type. Furthermore, in white noise with 0dB SNR, the proposed VAD still has about 90 percent accuracy.KeywordsVoice Activity DetectionPitchDynamic Noise EnvironmentHearing AidsMandarin Chinese