Bispectra Analysis-Based VAD for Robust Speech Recognition

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.

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.

Voice activity detection (VAD) is an outstanding problem for speech transmission, enhancement and recognition. The variety and the varying nature of speech and background noise make it especially challenging. In the past years, many features emphasizing the differences between speech and noise have been proposed for their robustness. However an important problem in many areas of speech processing is the determination of presence of speech periods in a given signal. This task can be identified as a statistical hypothesis problem and its purpose is the determination to which category or class a given signal belongs. Also the classification task is often not as trivial as it appears since the increasing level of background noise leading to numerous detection errors. The selection of an adequate feature vector for signal detection and a robust decision rule is a challenging problem that affects the performance of VADs. Most algorithms are effective in numerous applications but often cause detection errors mainly due to the loss of discriminating power of the decision rule at lower SNRs. In this paper, it has been tried to extract the characteristics of noise by the VAD algorithm which can be used to smooth out the signal in silence part from the noisy environment. For further noise reduction signal then filtered in the wavelet domain using thresholding.

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.

In recent years, DNN-based systems are extremely popular for performing automatic speech recognition (ASR) task. They have shown better performance than other methods. To perform ASR task efficiently, segmentation of the input data also has to be accurate. There are different kinds of methods of voice activity detection (VAD) including power based statistical methods. But to incorporate with the ASR system, it is better to perform the VAD task with the help of DNN-based method. In this paper, we investigate the superiority of DNN-based VAD over power based statistical methods. Not only that but also the effect of data augmentation on the performance of VAD in various conditions is also investigated. Performance of the VAD is evaluated using CENSREC-1-C data especially developed for evaluation of VAD task. The VAD trained with multi-condition data shows better performance than the baseline and other popular power based statistical VAD tools. We have also evaluated the performance for eval1 test dataset of CSJ corpus as well as its telephone variants.

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.

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 robust voice activity detector (VAD) is expected to increase the accuracy of ASR in noisy environments. This study focuses on how to extract robust information for designing a robust VAD. To do so, we construct a noise eigenspace by the principal component analysis of the noise covariance matrix. Projecting noise speech onto the eigenspace, it is found that available information with higher SNR is generally located in the channels with smaller eigenvalues. According to this finding, the available components of the speech are obtained by sorting the noise eigenspace. Based on the extracted high-SNR components, we proposed a robust voice activity detector. The threshold for deciding the available channels is determined using a histogram method. A probability-weighted speech presence is used to increase the reliability of the VAD. The proposed VAD is evaluated using TIMIT database mixed with a number of noises. Experiments showed that our algorithm performs better than traditional VAD algorithms.KeywordsVoice activity detectionPrincipal component analysisAuto-segmentationLocal noise estimation

In this paper, voice activity detection (VAD) algorithms and the development are introduced firstly, and then the analysis and comparison of the dominating VAD algorithms are proposed. Finally, the development characteristics of VAD are summarized. The simulation result shows that VAD of double threshold method is efficient on-line.

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.

Accurate and effective voice activity detection (VAD) is a fundamental step for robust speech or speaker recognition. In this study, we proposed a hierarchical framework approach for VAD and speech enhancement. The modified Wiener filter (MWF) approach is utilized for noise reduction in the speech enhancement block. For the feature selection and voting block, several discriminating features were employed in a voting paradigm for the consideration of reliability and discriminative power. Effectiveness of the proposed approach is compared and evaluated to other VAD techniques by using two well-known databases, namely, TIMIT database and NOISEX-92 database. Experimental results show that the proposed method performs well under a variety of noisy conditions.

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

Improved likelihood ratio test based voice activity detector applied to speech recognition

In normal human communication, people face the speaker when listening and usually pay attention to the speaker' face. Therefore, in robot audition, the recognition of the front talker is critical for smooth interactions. This paper presents an enhanced speech detection method for a humanoid robot that can separate and recognize speech signals originating from the front even in noisy home environments. The robot audition system consists of a new type of voice activity detection (VAD) based on the complex spectrum circle centroid (CSCC) method and a maximum signal-to-noise (Max-SNR) beamformer. This VAD based on CSCC can classify speech signals that are retrieved at the frontal region of two microphones embedded on the robot. The system works in real-time without needing training filter coefficients given in advance even in a noisy environment (SNR ≫ 0 dB). It can cope with speech noise generated from televisions and audio devices that does not originate from the center. Experiments using a humanoid robot, SIG2, with two microphones showed that our system enhanced extracted target speech signals more than 12 dB (SNR) and the success rate of automatic speech recognition for Japanese words was increased about 17 points.

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.