Analysis and detection of human emotion and stress from speech signals

Ph.DDOCTOR OF PHILOSOPH

Automatic speaker recognition: modelling, feature extraction and effects of clinical environment

Speaker recognition is the task of establishing identity of an individual based on his/her voice. It has a significant potential as a convenient biometric method for telephony applications and does not require sophisticated or dedicated hardware. The Speaker Recognition task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker-specific feature parameters from the speech. The features are used to generate statistical models of different speakers. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Current state of the art speaker recognition systems use the Gaussian mixture model (GMM) technique in combination with the Expectation Maximization (EM) algorithm to build the speaker models. The most frequently used features are the Mel Frequency Cepstral Coefficients (MFCC). This thesis investigated areas of possible improvements in the field of speaker recognition. The identified drawbacks of the current speaker recognition systems included: slow convergence rates of the modelling techniques and feature’s sensitivity to changes due aging of speakers, use of alcohol and drugs, changing health conditions and mental state. The thesis proposed a new method of deriving the Gaussian mixture model (GMM) parameters called the EM-ITVQ algorithm. The EM-ITVQ showed a significant improvement of the equal error rates and higher convergence rates when compared to the classical GMM based on the expectation maximization (EM) method. It was demonstrated that features based on the nonlinear model of speech production (TEO based features) provided better performance compare to the conventional MFCCs features. For the first time the effect of clinical depression on the speaker verification rates was tested. It was demonstrated that the speaker verification results deteriorate if the speakers are clinically depressed. The deterioration process was demonstrated using conventional (MFCC) features. The thesis also showed that when replacing the MFCC features with features based on the nonlinear model of speech production (TEO based features), the detrimental effect of the clinical depression on speaker verification rates can be reduced

Stress and emotion recognition in natural speech in the work and family environments

The speech stress and emotion recognition and classification technology has a potential to provide significant benefits to the national and international industry and society in general. The accuracy of an automatic emotion speech and emotion recognition relays heavily on the discrimination power of the characteristic features. This work introduced and examined a number of new linear and nonlinear feature extraction methods for an automatic detection of stress and emotion in speech. The proposed linear feature extraction methods included features derived from the speech spectrograms (SS-CB/BARK/ERB-AE, SS-AF-CB/BARK/ERB-AE, SS-LGF-OFS, SS-ALGF-OFS, SS-SP-ALGF-OFS and SS-sigma-pi), wavelet packets (WP-ALGF-OFS) and the empirical mode decomposition (EMD-AER). The proposed nonlinear feature extraction methods were based on the results of recent laryngological studies and nonlinear modelling of the phonation process. The proposed nonlinear features included the area under the TEO autocorrelation envelope based on different spectral decompositions (TEO-DWT, TEO-WP, TEO-PWP-S and TEO-PWP-G), as well as features representing spectral energy distribution of speech (AUSEES) and glottal waveform (AUSEEG). The proposed features were compared with features based on the classical linear model of speech production including F0, formants, MFCC and glottal time/frequency parameters. Two classifiers GMM and KNN were tested for consistency. The experiments used speech under actual stress from the SUSAS database (7 speakers; 3 female and 4 male) and speech with five naturally expressed emotions (neutral, anger, anxious, dysphoric and happy) from the ORI corpora (71 speakers; 27 female and 44 male). The nonlinear features clearly outperformed all the linear features. The classification results demonstrated consistency with the nonlinear model of the phonation process indicating that the harmonic structure and the spectral distribution of the glottal energy provide the most important cues for stress and emotion recognition in speech. The study also investigated if the automatic emotion recognition can determine differences in emotion expression between parents of depressed adolescents and parents of non-depressed adolescents. It was also investigated if there are differences in emotion expression between mothers and fathers in general. The experiment results indicated that parents of depressed adolescent produce stronger more exaggerated expressions of affect than parents of non-depressed children. And females in general provide easier to discriminate (more exaggerated) expressions of affect than males

Replay detection in voice biometrics: an investigation of adaptive and non-adaptive front-ends

Among various physiological and behavioural traits, speech has gained popularity as an effective mode of biometric authentication. Even though they are gaining popularity, automatic speaker verification systems are vulnerable to malicious attacks, known as spoofing attacks. Among various types of spoofing attacks, replay attack poses the biggest threat due to its simplicity and effectiveness. This thesis investigates the importance of 1) improving front-end feature extraction via novel feature extraction techniques and 2) enhancing spectral components via adaptive front-end frameworks to improve replay attack detection. This thesis initially focuses on AM-FM modelling techniques and their use in replay attack detection. A novel method to extract the sub-band frequency modulation (FM) component using the spectral centroid of a signal is proposed, and its use as a potential acoustic feature is also discussed. Frequency Domain Linear Prediction (FDLP) is explored as a method to obtain the temporal envelope of a speech signal. The temporal envelope carries amplitude modulation (AM) information of speech resonances. Several features are extracted from the temporal envelope and the FDLP residual signal. These features are then evaluated for replay attack detection and shown to have significant capability in discriminating genuine and spoofed signals. Fusion of AM and FM-based features has shown that AM and FM carry complementary information that helps distinguish replayed signals from genuine ones. The importance of frequency band allocation when creating filter banks is studied as well to further advance the understanding of front-ends for replay attack detection. Mechanisms inspired by the human auditory system that makes the human ear an excellent spectrum analyser have been investigated and integrated into front-ends. Spatial differentiation, a mechanism that provides additional sharpening to auditory filters is one of them that is used in this work to improve the selectivity of the sub-band decomposition filters. Two features are extracted using the improved filter bank front-end: spectral envelope centroid magnitude (SECM) and spectral envelope centroid frequency (SECF). These are used to establish the positive effect of spatial differentiation on discriminating spoofed signals. Level-dependent filter tuning, which allows the ear to handle a large dynamic range, is integrated into the filter bank to further improve the front-end. This mechanism converts the filter bank into an adaptive one where the selectivity of the filters is varied based on the input signal energy. Experimental results show that this leads to improved spoofing detection performance. Finally, deep neural network (DNN) mechanisms are integrated into sub-band feature extraction to develop an adaptive front-end that adjusts its characteristics based on the sub-band signals. A DNN-based controller that takes sub-band FM components as input, is developed to adaptively control the selectivity and sensitivity of a parallel filter bank to enhance the artifacts that differentiate a replayed signal from a genuine signal. This work illustrates gradient-based optimization of a DNN-based controller using the feedback from a spoofing detection back-end classifier, thus training it to reduce spoofing detection error. The proposed framework has displayed a superior ability in identifying high-quality replayed signals compared to conventional non-adaptive frameworks. All techniques proposed in this thesis have been evaluated on well-established databases on replay attack detection and compared with state-of-the-art baseline systems

Robust Phase-based Speech Signal Processing From Source-Filter Separation to Model-Based Robust ASR

The Fourier analysis plays a key role in speech signal processing. As a complex quantity, it can be expressed in the polar form using the magnitude and phase spectra. The magnitude spectrum is widely used in almost every corner of speech processing. However, the phase spectrum is not an obviously appealing start point for processing the speech signal. In contrast to the magnitude spectrum whose fine and coarse structures have a clear relation to speech perception, the phase spectrum is difficult to interpret and manipulate. In fact, there is not a meaningful trend or extrema which may facilitate the modelling process. Nonetheless, the speech phase spectrum has recently gained renewed attention. An expanding body of work is showing that it can be usefully employed in a multitude of speech processing applications. Now that the potential for the phase-based speech processing has been established, there is a need for a fundamental model to help understand the way in which phase encodes speech information. In this thesis a novel phase-domain source-filter model is proposed that allows for deconvolution of the speech vocal tract (filter) and excitation (source) components through phase processing. This model utilises the Hilbert transform, shows how the excitation and vocal tract elements mix in the phase domain and provides a framework for efficiently segregating the source and filter components through phase manipulation. To investigate the efficacy of the suggested approach, a set of features is extracted from the phase filter part for automatic speech recognition (ASR) and the source part of the phase is utilised for fundamental frequency estimation. Accuracy and robustness in both cases are illustrated and discussed. In addition, the proposed approach is improved by replacing the log with the generalised logarithmic function in the Hilbert transform and also by computing the group delay via regression filter. Furthermore, statistical distribution of the phase spectrum and its representations along the feature extraction pipeline are studied. It is illustrated that the phase spectrum has a bell-shaped distribution. Some statistical normalisation methods such as mean-variance normalisation, Laplacianisation, Gaussianisation and Histogram equalisation are successfully applied to the phase-based features and lead to a significant robustness improvement. The robustness gain achieved through using statistical normalisation and generalised logarithmic function encouraged the use of more advanced model-based statistical techniques such as vector Taylor Series (VTS). VTS in its original formulation assumes usage of the log function for compression. In order to simultaneously take advantage of the VTS and generalised logarithmic function, a new formulation is first developed to merge both into a unified framework called generalised VTS (gVTS). Also in order to leverage the gVTS framework, a novel channel noise estimation method is developed. The extensions of the gVTS framework and the proposed channel estimation to the group delay domain are then explored. The problems it presents are analysed and discussed, some solutions are proposed and finally the corresponding formulae are derived. Moreover, the effect of additive noise and channel distortion in the phase and group delay domains are scrutinised and the results are utilised in deriving the gVTS equations. Experimental results in the Aurora-4 ASR task in an HMM/GMM set up along with a DNN-based bottleneck system in the clean and multi-style training modes confirmed the efficacy of the proposed approach in dealing with both additive and channel noise

TOWARDS BUILDING GENERALIZABLE SPEECH EMOTION RECOGNITION MODELS

Abstract: Detecting the mental state of a person has implications in psychiatry, medicine, psychology and human-computer interaction systems among others. It includes (but is not limited to) a wide variety of problems such as emotion detection, valence-affect-dominance states prediction, mood detection and detection of clinical depression. In this thesis we focus primarily on emotion recognition. Like any recognition system, building an emotion recognition model consists of the following two steps: 1. Extraction of meaningful features that would help in classification 2. Development of an appropriate classifier Speech data being non-invasive and the ease with which it can be collected has made it a popular candidate for feature extraction. However, an ideal system designed should be agnostic to speaker and channel effects. While feature normalization schemes can counter these problems to some extent, we still see a drastic drop in performance when the training and test data-sets are unmatched. In this dissertation we explore some novel ways towards building models that are more robust to speaker and domain differences. Training discriminative classifiers involves learning a conditional distribution p(y_i|x_i), given a set of feature vectors x_i and the corresponding labels y_i; i=1,...N. For a classifier to be generalizable and not overfit to training data, the resulting conditional distribution p(y_i|x_i) is desired to be smoothly varying over the inputs x_i. Adversarial training procedures enforce this smoothness using manifold regularization techniques. Manifold regularization makes the model’s output distribution more robust to local perturbation added to a datapoint x_i. In the first part of the dissertation, we investigate two training procedures: (i) adversarial training where we determine the perturbation direction based on the given labels for the training data and, (ii) virtual adversarial training where we determine the perturbation direction based only on the output distribution of the training data. We demonstrate the efficacy of adversarial training procedures by performing a k-fold cross validation experiment on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and a cross-corpus performance analysis on three separate corpora. We compare their performances to that of a model utilizing other regularization schemes such as L1/L2 and graph based manifold regularization scheme. Results show improvement over a purely supervised approach, as well as better generalization capability to cross-corpus settings. Our second approach to better discriminate between emotions leverages multi-modal learning and automated speech recognition (ASR) systems toward improving the generalizability of an emotion recognition model that requires only speech as input. Previous studies have shown that emotion recognition models using only acoustic features do not perform satisfactorily in detecting valence level. Text analysis has been shown to be helpful for sentiment classification. We compared classification accuracies obtained from an audio-only model, a text-only model and a multi-modal system leveraging both by performing a cross-validation analysis on IEMOCAP dataset. Confusion matrices show it’s the valence level detection that is being improved by incorporating textual information. In the second stage of experiments, we used three ASR application programming interfaces (APIs) to get the transcriptions. We compare the performances of multi-modal systems using the ASR transcriptions with each other and with that of one using ground truth transcription. This is followed by a cross-corpus study. In the third part of the study we investigate the generalizability of generative of generative adversarial networks (GANs) based models. GANs have gained a lot of attention from machine learning community due to their ability to learn and mimic an input data distribution. GANs consist of a discriminator and a generator working in tandem playing a min-max game to learn a target underlying data distribution; when fed with data-points sampled from a simpler distribution (like uniform or Gaussian distribution). Once trained, they allow synthetic generation of examples sampled from the target distribution. We investigate the applicability of GANs to get lower dimensional representations from the higher dimensional feature vectors pertinent for emotion recognition. We also investigate their ability to generate synthetic higher dimensional feature vectors using points sampled from a lower dimensional prior. Specifically, we investigate two set ups: (i) when the lower dimensional prior from which synthetic feature vectors are generated is pre-defined, (ii) when the distribution of lower dimensional prior is learned from training data. We define the metrics that we used to measure and analyze the performance of these generative models in different train/test conditions. We perform cross validation analysis followed by a cross-corpus study. Finally we make an attempt towards understanding the relation between two different sub-problems encompassed under mental state detection namely depression detection and emotion recognition. We propose approaches that can be investigated to build better depression detection models by leveraging our ability to recognize emotions accurately

Multimodal analysis of verbal and nonverbal behaviour on the example of clinical depression

Clinical depression is a common mood disorder that may last for long periods, vary in severity, and could impair an individual’s ability to cope with daily life. Depression affects 350 million people worldwide and is therefore considered a burden not only on a personal and social level, but also on an economic one. Depression is the fourth most significant cause of suffering and disability worldwide and it is predicted to be the leading cause in 2020. Although treatment of depression disorders has proven to be effective in most cases, misdiagnosing depressed patients is a common barrier. Not only because depression manifests itself in different ways, but also because clinical interviews and self-reported history are currently the only ways of diagnosis, which risks a range of subjective biases either from the patient report or the clinical judgment. While automatic affective state recognition has become an active research area in the past decade, methods for mood disorder detection, such as depression, are still in their infancy. Using the advancements of affective sensing techniques, the long-term goal is to develop an objective multimodal system that supports clinicians during the diagnosis and monitoring of clinical depression. This dissertation aims to investigate the most promising characteristics of depression that can be “heard” and “seen” by a computer system for the task of detecting depression objectively. Using audio-video recordings of a clinically validated Australian depression dataset, several experiments are conducted to characterise depression-related patterns from verbal and nonverbal cues. Of particular interest in this dissertation is the exploration of speech style, speech prosody, eye activity, and head pose modalities. Statistical analysis and automatic classification of extracted cues are investigated. In addition, multimodal fusion methods of these modalities are examined to increase the accuracy and confidence level of detecting depression. These investigations result in a proposed system that detects depression in a binary manner (e.g. depressed vs. non-depressed) using temporal depression behavioural cues. The proposed system: (1) uses audio-video recordings to investigate verbal and nonverbal modalities, (2) extracts functional features from verbal and nonverbal modalities over the entire subjects’ segments, (3) pre- and post-normalises the extracted features, (4) selects features using the T-test, (5) classifies depression in a binary manner (i.e. severely depressed vs. healthy controls), and finally (6) fuses the individual modalities. The proposed system was validated for scalability and usability using generalisation experiments. Close studies were made of American and German depression datasets individually, and then also in combination with the Australian one. Applying the proposed system to the three datasets showed remarkably high classification results - up to a 95% average recall for the individual sets and 86% for the three combined. Strong implications are that the proposed system has the ability to generalise to different datasets recorded under quite different conditions such as collection procedure and task, depression diagnosis testing and scale, as well as cultural and language background. High performance was found consistently in speech prosody and eye activity in both individual and combined datasets, with head pose features a little less remarkable. Strong indications are that the extracted features are robust to large variations in recording conditions. Furthermore, once the modalities were combined, the classification results improved substantially. Therefore, the modalities are shown both to correlate and complement each other, working in tandem as an innovative system for diagnoses of depression across large variations of population and procedure

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies