Cepstral Mean And Variance Normalization Research Articles

ELM speaker identification for limited dataset using multitaper based MFCC and PNCC features with fusion score

In current scenario, speaker recognition under noisy condition is the major challenging task in the area of speech processing. Due to noise environment there is a significant degradation in the system performance. The major aim of the proposed work is to identify the speaker’s under clean and noise background using limited dataset. In this paper, we proposed a multitaper based Mel frequency cepstral coefficients (MFCC) and power normalization cepstral coefficients (PNCC) techniques with fusion strategies. Here, we used MFCC and PNCC techniques with different multitapers to extract the desired features from the obtained speech samples. Then, cepstral mean and variance normalization (CMVN) and Feature warping (FW) are the two techniques applied to normalize the obtained features from both the techniques. Furthermore, as a system model low dimension i-vector model is used and also different fusion score strategies like mean, maximum, weighted sum, cumulative and concatenated fusion techniques are utilized. Finally extreme learning machine (ELM) is used for classification in order to increase the system identification accuracy (SIA) intern which is having a single layer feedforward neural network with less complexity and time consuming compared to other neural networks. TIMIT and SITW 2016 are the two different databases are used to evaluate the proposed system under limited data of these databases. Both clean and noisy backgrounds conditions are used to check the SIA.

Convolutional neural network acoustic model for robust Indonesian speech recognition in noisy environment

Noise causes the decreasing accuracy of automatic speech recognition (ASR). Several techniques have been developed and proposed to overcome this problem. Using artificial neural network (ANN) as acoustic model is one of the techniques. Convolutional neural network (CNN) is a variant of ANN that has been used for acoustic modeling. Another approach is to do pre-processing to the speech signal or to the extracted acoustic feature from speech signal, such as cepstral mean and variance normalization (CMVN). On this work, CNN acoustic models were trained by using CMVN pre-processed acoustic feature to make a noise-robust speech recognition system. Two group of models were made, each to handle 2 kinds of noise (babble noise and street noise). Those acoustic models were tested with noisy speech at different SNR (signal-to-noise ratio) value. Testing results from CNN acoustic models were compared with the ones from Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) acoustic models. Testing results showed the increasing accuracy scores of acoustic models when models were trained using more variation of training data. CNN acoustic models that were trained using FBANK feature have higher accuracy scores than GMM-HMM models that were built using the same feature.

Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

In this study, a speaker identification system is considered consisting of a feature extraction stage which utilizes both power normalized cepstral coefficients (PNCCs) and Mel frequency cepstral coefficients (MFCC). Normalization is applied by employing cepstral mean and variance normalization (CMVN) and feature warping (FW), together with acoustic modeling using a Gaussian mixture model-universal background model (GMM-UBM). The main contributions are comprehensive evaluations of the effect of both additive white Gaussian noise (AWGN) and non-stationary noise (NSN) (with and without a G.712 type handset) upon identification performance. In particular, three NSN types with varying signal to noise ratios (SNRs) were tested corresponding to street traffic, a bus interior, and a crowded talking environment. The performance evaluation also considered the effect of late fusion techniques based on score fusion, namely, mean, maximum, and linear weighted sum fusion. The databases employed were TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3600 speech utterances. As recommendations from the study, mean fusion is found to yield overall best performance in terms of speaker identification accuracy (SIA) with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings.

Evaluation of MSR Identity Toolbox under conditions reflecting those of a real forensic case (forensic_eval_01)

This paper presents an evaluation of the Microsoft Research Identity Toolbox version 1.0 developed at Microsoft Research, as a tool for forensic voice comparison under conditions reflecting those of a real forensic case. For this purpose we implement two systems: the first is based on Gaussian mixture model - universal background model (GMM-UBM) and the second on i-vectors with probabilistic linear discriminant analysis (i-vector PLDA). Three different feature-level mismatch compensation techniques were tested, before and after the application of voice activity detection (VAD). The three techniques were global cepstral mean subtraction (CMS), global cepstral mean and variance normalization (CMVN), and local feature warping (FW).

Modified Mean and Variance Normalization: Transforming to Utterance-Specific Estimates

Cepstral mean and variance normalization (CMVN) is an efficient noise compensation technique popularly used in many speech applications. CMVN eliminates the mismatch between training and test utterances by transforming them to zero mean and unit variance. In this work, we argue that some amount of useful information is lost during normalization as every utterance is forced to have the same first- and second-order statistics, i.e., zero mean and unit variance. We propose to modify CMVN methodology to retain the useful information and yet compensate for noise. The proposed normalization approach transforms every test utterance to utterance-specific clean mean (i.e., utterance mean if the noise was absent) and clean variance, instead of zero mean and unit variance. We derive expressions to estimate the clean mean and variance from a noisy utterance. The proposed normalization is effective in the recognizing voice commands that are typically short (single words or short phrases), where more advanced methods [such as histogram equalization (HEQ)] are not effective. Recognition results show a relative improvement (RI) of $$21\,\%$$21% in word error rate over conventional CMVN on the Aurora-2 database and a RI of 20 and $$11\,\%$$11% over CMVN and HEQ on short utterances of the Aurora-2 database.

강인한 음성인식을 위한 극점 필터링 및 스케일 정규화를 이용한 켑스트럼 특징 정규화 방식

In this paper, the pole filtering concept is applied to the Mel-frequency cepstral coefficient (MFCC) feature vectors in the conventional cepstral mean normalization (CMN) and cepstral mean and variance normalization (CMVN) frameworks. Additionally, performance of the cepstral mean and scale normalization (CMSN), which uses scale normalization instead of variance normalization, is evaluated in speech recognition experiments in noisy environments. Because CMN and CMVN are usually performed on a per-utterance basis, in case of short utterance, they have a problem that reliable estimation of the mean and variance is not guaranteed. However, by applying the pole filtering and scale normalization techniques to the feature normalization process, this problem can be relieved. Experimental results using Aurora 2 database (DB) show that feature normalization method combining the pole-filtering and scale normalization yields the best improvements.

Modulation Spectrum Equalization for Improved Robust Speech Recognition

We propose novel approaches for equalizing the modulation spectrum for robust feature extraction in speech recognition. Common to all approaches in that the temporal trajectories of the feature parameters are first transformed into the magnitude modulation spectrum. In spectral histogram equalization (SHE) and two-band spectral histogram equalization (2B-SHE), we equalize the histogram of the modulation spectrum for each utterance to a reference histogram obtained from clean training data, or perform the equalization with two sub-bands on the modulation spectrum. In magnitude ratio equalization (MRE), we define the magnitude ratio of lower to higher modulation frequency components for each utterance, and equalize this to a reference value obtained from clean training data. These approaches can be viewed as temporal filters that are adapted to each testing utterance. Experiments performed on the Aurora 2 and 4 corpora for small and large vocabulary tasks indicate that significant performance improvements are achievable for all noise conditions. We also show that additional improvements can be obtained when these approaches are integrated with cepstral mean and variance normalization (CMVN), histogram equalization (HEQ), higher order cepstral moment normalization (HOCMN), or the advanced front-end (AFE). We analyze and discuss the reasons for these improvements from different viewpoints with different sets of data, including adaptive temporal filtering, noise behavior on the modulation spectrum, phoneme types, and modulation spectrum distance measures.

A temporal warped 2D psychoacoustic modeling for robust speech recognition system

Human auditory system performs better than speech recognition system under noisy condition, which leads us to the idea of incorporating the human auditory system into automatic speech recognition engines. In this paper, a hybrid feature extraction method, which utilizes forward masking, backward masking, and lateral inhibition, is incorporated into mel-frequency cepstral coefficients (MFCC). The integration is implemented using a warped 2D psychoacoustic filter. The AURORA2 database is utilized for testing, and the Hidden Markov Model (HMM) is used for recognition. Comparison is made against lateral inhibition (LI), forward masking (FM), cepstral mean and variance normalization (CMVN), the original 2D psychoacoustic filter and the RASTA filter. Experimental results show that the word recognition rate is significantly improved, especially under noisy conditions.

Constructing Modulation Frequency Domain-Based Features for Robust Speech Recognition

Data-driven temporal filtering approaches based on a specific optimization technique have been shown to be capable of enhancing the discrimination and robustness of speech features in speech recognition. The filters in these approaches are often obtained with the statistics of the features in the temporal domain. In this paper, we derive new data-driven temporal filters that employ the statistics of the modulation spectra of the speech features. Three new temporal filtering approaches are proposed and based on constrained versions of linear discriminant analysis (LDA), principal component analysis (PCA), and minimum class distance (MCD), respectively. It is shown that these proposed temporal filters can effectively improve the speech recognition accuracy in various noise-corrupted environments. In experiments conducted on Test Set A of the Aurora-2 noisy digits database, these new temporal filters, together with cepstral mean and variance normalization (CMVN), provide average relative error reduction rates of over 40% and 27% when compared with baseline Mel frequency cepstral coefficient (MFCC) processing and CMVN alone, respectively.

Optimization of temporal filters for constructing robust features in speech recognition

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.