Robust Front-End Based on MVA and HEQ Post-processing for Arabic Speech Recognition Using Hidden Markov Model Toolkit (HTK)

Most existing speech forensic works implicitly assume the suspected speech either has or has not been processed by a specific operation. In practice, however, the operation type performed on the target speech is usually unknown to the forensic analyst, and in most cases, multiple operations may be involved in order to conceal the forgery trace. Few works have considered these issues. In this study, we propose a universal forensic algorithm that can detect four typical speech operations: pitch shifting, noise-adding, low-pass filtering, and high-pass filtering. The motivation of the proposed algorithm is based on the observation that different operations will cause different effects on Mel-frequency cepstral coefficients (MFCC). The statistical moments of MFCC are extracted as detecting features. Additionally, cepstral mean and variance normalization (CMVN), which is a computationally efficient normalization technique, is used to eliminate the impact of channel noise. Finally, an ensembled binary classifier is used to detect the type of various operations, and multiclass classifiers are adopted to identify the order of operations. The experimental results on the TIMIT and UME-ERJ datasets show that the proposed forensic features achieve good performance on the operation type and order detection. Additionally, the results demonstrate the effectiveness of the proposed algorithm in terms of robustness against the MP3 compression attack.

The present work investigates the robustness of Power Normalized Cepstral Coefficients (PNCC) for Language identification (LID) from noisy speech. Though the state of the art vocal tract features like mel frequency cepstral coefficients (MFCC) give good recognition accuracy in clean environments, the performance degrades drastically when the signal to noise ratio decreases. In this work, experiments have been carried out on IITKGP-MLILSC speech database. Gaussian mixture model (GMM) is used to building the language models. We have used NOISEX-92 database to add synthetic noise at different SNR levels. We have also compared the recognition accuracy of two systems, one developed using MFCCs and and the other using PNCCs. Finally, we have shown that PNCC features are more robust to noise.

The performance of automatic speech recognition (ASR) systems is often seriously degraded by noise interference. Among the techniques to reduce the noise effect, cepstral mean-and-variance normalization (CMVN) is a simple yet quite effective approach for processing MFCC speech features. However, the features processed by CMVN contain a significant number of outliers, which very likely weakens the effect of CMVN. This paper primarily proposes to deal with the outliers left by CMVN with two directions. The first one is to apply a sigmoid function transformation, which provides explicit lower and upper bounds for the outliers, and the second one exploits the well-known median filter to remove the impulse-like outliers in the CMVN features. Under the Aurora-2 digit recognition database and task, the presented two frameworks give rise to around 5% in absolute accuracy improvement in comparison with CMVN, and the corresponding word error rate reduction relative to the MFCC baseline is as high as 50%.

In speaker verification, it is necessary to reduce the influence of different environmental conditions. In this paper, two stages of normalization techniques, feature normalization and score normalization, are examined for decreasing the mismatch between training and testing acoustic conditions. At the first stage, cepstral mean and variance normalization (CMVN) is modified to normalize the cepstral coefficients with the similar segmental parameter statistics. Next, due to score variability between verification trials, Test-dependent zero-score normalization (TZnorm) and Zero-dependent test-score normalization (ZTnorm) are comparatively presented to transform the output scores entirely and make the speaker-independent decision threshold more robust under adverse conditions. Experiments on NIST2002 SRE corpus show that the normalizations with CMVN in feature stage and ZTnorm in score stage achieved 20.3% relative reduction of EER and 18.1% relative reduction of the minimal DCF compared to the baseline system using CMN and zero normalization.

Linear discriminant analysis (LDA) has long been used to derive data-driven temporal filters in order to improve the robustness of speech features used in speech recognition. In this paper, we proposed the use of new optimization criteria of principal component analysis (PCA) and the minimum classification error (MCE) for constructing the temporal filters. Detailed comparative performance analysis for the features obtained using the three optimization criteria, LDA, PCA, and MCE, with various types of noise and a wide range of SNR values is presented. It was found that the new criteria lead to superior performance over the original MFCC features, just as LDA-derived filters can. In addition, the newly proposed MCE-derived filters can often do better than the LDA-derived filters. Also, it is shown that further performance improvements are achievable if any of these LDA/PCA/MCE-derived filters are integrated with the conventional approach of cepstral mean and variance normalization (CMVN). The performance improvements obtained in recognition experiments are further supported by analyses conducted using two different distance measures.

Significance of analytic phase of speech signals in speaker verification

Feature extraction techniques for input representation to diagnose infant diseases have received significant attention recently. Mel Frequency Cepstral Coefficients (MFCC) is one of the most popular feature extraction techniques due to its representation method being very similar to the human auditory system. The MFCC method for feature extraction depends on several important parameter settings, namely the number of filter banks, and the number of coefficients used in the final representation. These settings affects the way the features are represented, and in turn, affects the performance of the classifier for diagnosis of the disease. In this paper, the Particle Swarm Optimization (PSO) algorithm was used to optimise the parameters of the MFCC feature extraction method for classifying infants with asphyxia. The extracted MFCC features were then used to train several MLP classifiers over different initialization values. The accuracy of these classifiers was then used to guide the PSO optimization. Our results show that the optimization of MFCC computation using PSO yielded 93.9% accuracy, an improvement of 1.45% over typical MFCC parameter settings using the same classifier.

In this paper, we present an efficient parallel implementation of Mel-frequency Cepstral Coefficient (MFCC)-based feature extraction and describe the optimizations required for effective throughput on Graphics Processing Units (GPU) processors. We demonstrate that the feature extraction process in automatic speech recognition is well suited for GPUs and a substantial reduction in computation time can be obtained by performing feature extraction on these platforms. Using a single Nvidia GTX580 GPU our proposed approach is shown to be approximately 90x faster than a sequential CPU implementation, enabling feature extraction to be performed at under 0.01% real-time. This is significantly faster than prior reported results implemented on GPUs, DSPs and FPGAs. Furthermore we demonstrate that multiple MFCC features can be generated for a set of predefined Vocal-Tract-Length-Normalization (VTLN) alpha parameters with little degradation in throughput. Using the approach described in this paper MFCC features were extracted in 0.05% and 0.09% realtime, for 11 and 21 VTLN parameters respectively. (4 pages)

Focused on the issue that the robustness of traditional Mel Frequency Cepstral Coefficients (MFCC) feature degrades drastically in speaker recognition system, a kind algorithm that based improved Gammatone Frequency Cepstral Coefficients (GFCC) is proposed. The different between traditional MFCC and GFCC is that GFCC uses Gammatone filter bank to replace Mel filter bank to improve robustness. On this basis, this paper proposes one way that use Multitaper Estimation, MVA (Mean Subtraction, Variance Normzlization and Autoregressive Moving Average Filter)and other technologies to further enhance its robustness and tested with TIMIT speech database. The experimental results show that under different noise and different SNR, the improved GFCC that proposed by this paper has the lowest equal error rate and the best robustness, especially in the noise ratio is lower than 10dB, has greater advantage compared to other algorithms.

Cosine distance similarities with a set of reference speakers, cosine distance features (CDF), with a backend support vector machine classifier (CDF-SVM) have been explored in our earlier studies for improving the performance of speaker verification systems. Subsequently, we also investigated on its effectiveness in improving the noise robustness of speaker verification systems. In this work, we study how the performance of CDF-SVM systems can be further improved by weighting the feature vectors using latent semantic information (LSI) technique. We use mel frequency cepstral coefficients (MFCC), power normalized cepstral coefficients (PNCC), or delta spectral cepstral coefficients (DSCC) for deriving CDF. Experimental results on the female part of short2-short3 trials of NIST speaker recognition evaluation dataset show that the proposed weighted CDF-SVM system outperforms the baseline i-vector with cosine distance scoring (i-CDS), i-vector with a backend SVM classifier (i-SVM) and CDF-SVM systems. Finally, we fused the weighted CDF-SVM with i-CDS and the performance of the combined system was evaluated under different stationary and non-stationary additive noise test conditions. It was seen that the noise robustness of the fused weighted CDF-SVM+i-CDS system is significantly better than the individual systems and the fused CDF-SVM+i-CDS of our earlier work in both clean and noisy test environments except for the zero SNR level condition of certain noises.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Speech production knowledge has been used to enhance the phonetic representation and the performance of automatic speech recognition (ASR) systems successfully. Representations of speech production make simple explanations for many phenomena observed in speech. These phenomena can not be easily analyzed from either acoustic signal or phonetic transcription alone. One of the most important aspects of speech production knowledge is the use of articulatory knowledge, which describes the smooth and continuous movements in the vocal tract. In this paper, we present a new articulatory model to provide available information for rescoring the speech recognition lattice hypothesis. The articulatory model consists of a feature front-end, which computes a voicing feature based on a spectral harmonics correlation (SHC) function, and a back-end based on the combination of deep neural networks (DNNs) and hidden Markov models (HMMs). The voicing features are incorporated with standard Mel frequency cepstral coefficients (MFCCs) using heteroscedastic linear discriminant analysis (HLDA) to compensate the speech recognition accuracy rates. Moreover, the advantages of two different models are taken into account by the algorithm, which retains deep learning properties of DNNs, while modeling the articulatory context powerfully through HMMs. Mandarin speech recognition experiments show the proposed method achieves significant improvements in speech recognition performance over the system using MFCCs alone.

In this paper, we propose a feature set by concatenating Mel-Frequency Cepstral Coefficients (MFCC) and Spectral Centroid Magnitude Coefficients (SCMC) features for Acoustic Scene Classification (ASC) using Deep Neural Networks (DNN). MFCC features are used to hold the acoustic characteristics such as spectral envelope of an acoustic scene in each frame. It also carries the sub-band average energy as a single dimension. SCMC features are used to hold the distribution of energy in a sub-band effectively. A test is carried out on Tampere University of Technology (TUT) Acoustic Scenes 2017 Dataset. The DNN architecture for utterance level classification has been used. The proposed system’s performance on a 4-fold cross-validation setup is 80.2% and it gives 5.4% relative improvement in performance when compared to the baseline system that uses log-Mel band energies with Multi-Layer Perceptron model.

The mel-frequency cepstral coefficient (MFCC) or perceptual linear prediction (PLP) feature extraction typically used for automatic speech recognition (ASR) employ several principles which have known counterparts in the cochlea and auditory nerve: frequency decomposition, mel- or bark-warping of the frequency axis, and compression of amplitudes. It seems natural to ask if one can profitably employ a counterpart of the next physiological processing step, synaptic adaptation. We, therefore, incorporated a simplified model of short-term adaptation into MFCC feature extraction. We evaluated the resulting ASR performance on the AURORA 2 and AURORA 3 tasks, in comparison to ordinary MFCCs, MFCCs processed by RASTA, and MFCCs processed by cepstral mean subtraction (CMS), and both in comparison to and in combination with Wiener filtering. The results suggest that our approach offers a simple, causal robustness strategy which is competitive with RASTA, CMS, and Wiener filtering and performs well in combination with Wiener filtering. Compared to the structurally related RASTA, our adaptation model provides superior performance on AURORA 2 and, if Wiener filtering is used prior to both approaches, on AURORA 3 as well.

Frequency masking filtering is introduced in a standard mel frequency cepstral coefficients (MFCC) feature extraction algorithm. It mimics a human masking mechanism to get more robust features when the input speech is distorted by various noises. The AURORA 2.0 database together with HTK speech recognition toolkits are used to evaluate the impact of the frequency masking filtering algorithm at various thresholds. It is shown that with the proper frequency masking coefficients, it can have about 6.59%, 6.01% and 1.20% relative performance improvements over standard MFCC for test A and test B and test C respectively, in clean-condition training. It works well on all eight different noise conditions. It has also proved to be effective when it is combined with other popular noise robust techniques, such as cepstral mean normalization. The proposed frequency masking filtering algorithm is fairly simple and it only requires a very small extra computation load.