Speech enhancement in noisy environment using voice activity detection and wavelet thresholding

An important drawback affecting most of the speech processing systems is the environmental noise and its harmful effect on the system performance. Examples of such systems are the new wireless communications voice services or digital hearing aid devices. In speech recognition, there are still technical barriers inhibiting such systems from meeting the demands of modern applications. Numerous noise reduction techniques have been developed to palliate the effect of the noise on the system performance and often require an estimate of the noise statistics obtained by means of a precise voice activity detector (VAD). Speech/non-speech detection is an unsolved problem in speech processing and affects numerous applications including robust speech recognition (Karray and Marting, 2003; Ramirez et al. 2003), discontinuous transmission (ITU, 1996; ETSI, 1999), real-time speech transmission on the Internet (Sangwan et al., 2002) or combined noise reduction and echo cancellation schemes in the context of telephony (Basbug et al., 2004; Gustafsson et al., 2002). The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. During the last decade, numerous researchers have developed different strategies for detecting speech on a noisy signal (Sohn et al., 1999; Cho and Kondoz, 2001; Gazor and Zhang, 2003, Armani et al., 2003) and have evaluated the influence of the VAD effectiveness on the performance of speech processing systems (Bouquin-Jeannes and Faucon, 1995). Most of the approaches have focussed on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules (Woo et al., 2000; Li et al., 2002; Marzinzik and Kollmeier, 2002). The different VAD methods include those based on energy thresholds (Woo et al., 2000), pitch detection (Chengalvarayan, 1999), spectrum analysis (Marzinzik and Kollmeier, 2002), zero-crossing rate (ITU, 1996), periodicity measure (Tucker, 1992), higher order statistics in the LPC residual domain (Nemer et al., 2001) or combinations of different features (ITU, 1993; ETSI, 1999; Tanyer and Ozer, 2000). This chapter shows a comprehensive approximation to the main challenges in voice activity detection, the different solutions that have been reported in a complete review of the state of the art and the evaluation frameworks that are normally used. The application of VADs for speech coding, speech enhancement and robust speech recognition systems is shown and discussed. Three different VAD methods are described and compared to standardized and

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.

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.

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 and effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The approach is based on filtering the input channel to avoid high energy noisy components and then the determination of the speech/non-speech bispectra by means of third order auto-cumulants. This algorithm differs from many others in the way the decision rule is formulated (detection tests) and the domain used in this approach. Clear improvements in speech/non-speech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It is shown that application of statistical detection test leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The algorithm also incorporates a previous noise reduction block improving the accuracy in detecting speech and non-speech. The experimental analysis carried out on the AURORA databases and tasks provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs such as ITU G.729, GSM AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs.

Voice activity detection (VAD) in noisy environments is a very important preprocessing scheme in speech communication technology, a field which includes speech recognition, speech coding, speech enhancement and captioning video contents. We have developed a VAD method for noisy environments based on the modulation spectrum. In Experiment 1, we investigate the optimal ranges of speech and modulation frequencies for the proposed algorithm by using the simulated data in the CENSREC-1-C corpus. Results show that when we combine an upper limit frequency between 1,000 and 2,000 Hz with a lower limit frequency of less than 300 Hz as speech frequency bands, error rates are lower than with other bands. Furthermore, when we use the frequency components of the modulation spectrum between 3–9, 3–11, 3–14, 3–18, 4–9, 4–11, 4–14, 4–18, 5–7, 5–9, 5–11, or 5–14 Hz, the proposed method performs VAD well. In Experiment 2, we use one of the best parameter settings from Experiment 1 and evaluate the real environment data in the CENSREC-1-C corpus by comparing our method with other conventional methods. Improvements were observed from the VAD results for each SNR condition and noise type.

This paper proposes robust feature extraction for accurate voice activity detection (VAD). VAD is one of the principal modules for speech signal processing such as speech codec, speech enhancement, and speech recognition. Noisy environments contain nonstationary noises causing the accuracy of the VAD to drastically decline because the fluctuation of features in the noise intervals results in increased false alarm rates. In this paper, in order to improve the VAD performance, harmonic-weighted energy is proposed. This feature extraction method focuses on voiced speech intervals and weighted harmonic-to-noise ratios to determine the amount of the harmonicity to frame energy. For performance evaluation, the receiver operating characteristic curves and equal error rate are measured.

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.

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.

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.

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.

Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in both noisy and noise-free environments, while not introducing significant latency. In this work we propose using an adversarial multi-task learning method when training a supervised VAD. The method has been applied to the state-of-the-art VAD Waveform-based Voice Activity Detection. Additionally the performance of the VADis investigated under different algorithmic delays, which is an important factor in latency. Introducing adversarial multi-task learning to the model is observed to increase performance in terms of Area Under Curve (AUC), particularly in noisy environments, while the performance is not degraded at higher SNR levels. The adversarial multi-task learning is only applied in the training phase and thus introduces no additional cost in testing. Furthermore the correlation between performance and algorithmic delays is investigated, and it is observed that the VAD performance degradation is only moderate when lowering the algorithmic delay from 398 ms to 23 ms.

To obtain reliable performance of Voice Activity Detector (VAD) algorithm, the straight lines on spectrogram of speech-activity being robust against noise is characterized by an entropy-based measure in this paper. A measure of entropy will be defined on the energy domain of harmonic subband. It is shown that the entropy-based measure is well suited for detecting speech in white or quasi-white noises, but will perform poorly for coloured noises. To compensate the limitation, the refined minima controlled recursive averaging, which be updated quickly and accurately even given rapidly increasing levels of noise, is required to desensitize the measure of entropy to various types of noise. Consequently, the proposed VAD algorithm is shown significantly outperform the commonly used energy-based algorithm when SNR drops rapidly, and moreover is insensitive to the changing-level of noise. Experimental results demonstrate that the performance of the proposed VAD is comparable to modern standard VADs such that ITU-T G.729B and ETSI front-end VAD or statistical model-based VADs.

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

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.