Optimization of temporal filters for constructing robust features in speech recognition

This paper describes a study of a set of features based on cepstral mean and variance normalization (CMVN) plus auto regressive moving average (ARMA) filtering technique which is called MVA and on histogram equalization (HEQ) for robust speech recognition. First, we use MVA then HEQ in combination with CMVN and ARMA filtering as a post-processing module to mel frequency cepstral coefficients (MFCC), Relative Spectral-Perceptual linear prediction (RASTA-PLP) and power normalized cepstral coefficients (PNCC) features to improve the performance of the automatic speech recognition (ASR) system. The results on the Arabic database task have shown that both methods MVA and HEQ+ARMA improves the success rate for all features compared to the baseline system however HEQ was not found to perform better than MVA. The results also provide that RASTA-PLP outperforms PNCC and MFCC features.

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.

Dimensionality reduction is an important problem in pattern recognition. In a speech recognition system, the size of the feature set is normally large in the order of 40. Therefore, it is necessary to reduce the dimensionality of the feature space for efficient and effective speech recognition. Two popular methods to reduce the dimensionality of the feature space are linear discriminat analysis (LDA) and principal component analysis (PCA). This paper uses the minimum error classification (MCE) training algorithm for dimensionality reduction and presents an alternative MCE training algorithm that performs better on testing data than the conventional MCE training algorithm. The effects of the initial value of the transformation matrix on the performance of MCE have also been studied.

In this work, we propose a novel feature extraction algorithm that improves the robustness of automatic speech recognition (ASR) systems in the presence of various types of noise. The proposed algorithm uses a new cepstral technique based on the differential power spectrum (DPS) instead of the power spectrum (PS), the algorithm replaces the logarithmic non linearity by the power function. In order to reduce cepstral coefficients mismatches between training and testing conditions, we used the mean and variance normalization, then we apply auto-regression movingaverage filtering (MVA) in the cepstral domain. The ASR experiments were conducted using two databases, the first is LASA digit database designed for recognition the isolated Arabic digits in the presence of different types of noise. The second is Aurora 2 noisy speech database designed to recognize connected English digits in various operating environments. The experimental results show a substantial improvement from the proposed algorithm over the baseline Mel Frequency Cepstral Coefficients (MFCC), the relative improvement is the 28.92% for LASA database and is the 44.43% for aurora 2 database. The performance of our proposed algorithm was tested and verified by extensive comparisons with the state-of-the-art noise-robust features in aurora 2.

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

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%.

This research presents a development of the affine projection algorithm (APA) in voice communication applications. A method of controlling the parameters of the APA is devised to improve the performance in cancelling various types of ambient noise that could possibly corrupt speech signals in voice communication systems. Indicators are used to identify the type of noise accompanying the target signal. Then the corrupted signal is processed in a noise a cancellation setup in such a way that three parameters of algorithm are changed according to the nature of the noise. The spreading of elements in the covariance matrix of the noise is used as an indicator for the type of noise so that the projection order, step-size and filter length are changed at the same time. This way the performance of the canceller is improved rendering lower estimation error with a moderate computational power. The method was tested under various types of noise and showed better convergence performance than the original APA as well as other commonly used algorithms in noise cancellation systems. The MSE of the proposed VPAPA method drops to -65 dB in steady state compared to -20 dB using NLMS and just below -30 dB using standard APA with projection order of 8, while the powerful RLS reaches around -60dB under the same environment. The method can be useful for clearer voice communication in variable environmental noise.

We describe an audio-visual automatic continuous speech recognition system, which significantly improves speech recognition performance over a wide range of acoustic noise levels, as well as under clean audio conditions. The system utilizes facial animation parameters (FAPs) supported by the MPEG-4 standard for the visual representation of speech. We also describe a robust and automatic algorithm we have developed to extract FAPs from visual data, which does not require hand labeling or extensive training procedures. The principal component analysis (PCA) was performed on the FAPs in order to decrease the dimensionality of the visual feature vectors, and the derived projection weights were used as visual features in the audio-visual automatic speech recognition (ASR) experiments. Both single-stream and multistream hidden Markov models (HMMs) were used to model the ASR system, integrate audio and visual information, and perform a relatively large vocabulary (approximately 1000 words) speech recognition experiments. The experiments performed use clean audio data and audio data corrupted by stationary white Gaussian noise at various SNRs. The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only speech recognition WERs, at various SNRs (0–30 dB) with additive white Gaussian noise, and by 19% relatively to audio-only speech recognition WER under clean audio conditions.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

The contribution of type and levels of community noises to the perception of loudness, noisiness, and annoyance is being explored in ongoing research projects. Particular attention has been given to the relationship between scaling method and the conclusions it is possible to draw from the data, especially with regard to observers' abilities to differentiate noises according to the above attributes. Procedures for calibrating and possibly standardizing the perceptual scales have been attempted with preliminary success. Various types of noises have been investigated: aircraft noise under different flying conditions, traffic noise, and a number of other community noises. Besides specific scaling problems, the investigations have addressed the specific problems of observers' abilities to differentiate among noises that are combined in a complex setting as well as their ability to integrate various noises into a unitary perception of loudness and annoyance. Since the experimental setups reflect real‐life situations, the answers to these latter questions are relevant to understanding the responses of the individual who is exposed to combinations of noises in his or her community.The contribution of type and levels of community noises to the perception of loudness, noisiness, and annoyance is being explored in ongoing research projects. Particular attention has been given to the relationship between scaling method and the conclusions it is possible to draw from the data, especially with regard to observers' abilities to differentiate noises according to the above attributes. Procedures for calibrating and possibly standardizing the perceptual scales have been attempted with preliminary success. Various types of noises have been investigated: aircraft noise under different flying conditions, traffic noise, and a number of other community noises. Besides specific scaling problems, the investigations have addressed the specific problems of observers' abilities to differentiate among noises that are combined in a complex setting as well as their ability to integrate various noises into a unitary perception of loudness and annoyance. Since the experimental setups reflect real‐life situ...

Minimum classification error (MCE) based discriminative methods have been extensively studied and successfully applied to automatic speech recognition, speaker recognition, and utterance verification. Our goal is to modify the embedded string model based MCE algorithm to handle the case of a large number of cross-syllable trip hones used in a large vocabulary recognition system. A selective strategy about MCE based discriminative training method, in particular for Mandarin speech syllable loop recognition, is introduced. We use a syllable loop recognition task to evaluate the performance of the acoustic model of an established large vocabulary continuous speech recognition system. The basic idea is that since decoding errors occur in parts of the models in whole decoded sentences, it is reasonable to adjust the parameters of the wrong models. As a result, a weighted MCE formulation is derived, which can provide more effective convergence and about a 10% error rate reduction for a large training set. Our experiments show that, although the performance of the recognition system is improved, some original correct recognition results are misrecognized after discriminative training, and a divide and conquer strategy is proposed as a solution. Thus, the acoustic feature space is divided into two or more sub-spaces according to the discriminative training procedure. By combining these two methods, we obtain over 14.5% error rate reduction in syllable loop recognition.

Keyword spotting can be formulated as a non-uniform error automatic speech recognition (ASR) problem. It has been demonstrated [1] that this new formulation with the nonuniform MCE training technique can lead to improved system performance in keyword spotting applications. In this paper, we demonstrate that deep neural networks (DNNs) can be successfully trained on the non-uniform minimum classification error (MCE) criterion which weighs the errors on keywords much more significantly than those on non-keywords in an ASR task. The integration with a DNN-HMM system enables modeling of multi-frame distributions, which conventional systems find difficult to accomplish. To further improve the performance, more confusable data is generated by boosting the likelihood of the sentences that have more errors. The keyword spotting system is implemented within a weighted finite state transducer (WFST) framework and the DNN is optimized using standard backpropagation and stochastic gradient decent. We evaluate the performance of the proposed framework on a large vocabulary spontaneous conversational telephone speech dataset (Switchboard-1 Release 2). The proposed approach achieves an absolute figure of merit improvement of 3.65% over the baseline system.

The minimum classification error (MCE) framework for discriminative training is a simple and general formalism for directly optimizing recognition accuracy in pattern recognition problems. The framework applies directly to the optimization of hidden Markov models (HMMs) used for speech recognition problems. However, few if any studies have reported results for the application of MCE training to large-vocabulary, continuous-speech recognition tasks. This article reports significant gains in recognition performance and model compactness as a result of discriminative training based on MCE training applied to HMMs, in the context of three challenging large-vocabulary (up to 100 k word) speech recognition tasks: the Corpus of Spontaneous Japanese lecture speech transcription task, a telephone-based name recognition task, and the MIT Jupiter telephone-based conversational weather information task. On these tasks, starting from maximum likelihood (ML) baselines, MCE training yielded relative reductions in word error ranging from 7% to 20%. Furthermore, this paper evaluates the use of different methods for optimizing the MCE criterion function, as well as the use of precomputed recognition lattices to speed up training. An overview of the MCE framework is given, with an emphasis on practical implementation issues

Combined speech enhancement and auditory modelling for robust distributed speech recognition

A feature extraction and fusion algorithm was constructed by combining principal component analysis (PCA) and linear discriminant analysis (LDA) to detect a fault state of the induction motor. After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment, the reference data were used to produce matching values. In a diagnostic step, two matching values that were obtained by PCA and LDA, respectively, were combined by probability model, and a faulted signal was finally diagnosed. As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief, it shows excellent performance under the noisy environment. The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.