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

Idealized computational models for auditory receptive fields

This paper presents a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to enable invariance of receptive field responses under natural sound transformations and ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequency transformation of a purely temporal sound signal, it is shown that the framework allows for a new way of deriving the Gabor and Gammatone filters as well as a novel family of generalized Gammatone filters, with additional degrees of freedom to obtain different trade-offs between the spectral selectivity and the temporal delay of time-causal temporal window functions. When applied to the definition of a second-layer of receptive fields from a spectrogram, it is shown that the framework leads to two canonical families of spectro-temporal receptive fields, in terms of spectro-temporal derivatives of either spectro-temporal Gaussian kernels for non-causal time or the combination of a time-causal generalized Gammatone filter over the temporal domain and a Gaussian filter over the logspectral domain. For each filter family, the spectro-temporal receptive fields can be either separable over the time-frequency domain or be adapted to local glissando transformations that represent variations in logarithmic frequencies over time. Within each domain of either non-causal or time-causal time, these receptive field families are derived by uniqueness from the assumptions. It is demonstrated how the presented framework allows for computation of basic auditory features for audio processing and that it leads to predictions about auditory receptive fields with good qualitative similarity to biological receptive fields measured in the inferior colliculus (ICC) and primary auditory cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

A survey on the feasibility of sound classification on wireless sensor nodes

Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert

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

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

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. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference