193 research outputs found
Detecting emotions from speech using machine learning techniques
D.Phil. (Electronic Engineering
Disentangling Prosody Representations with Unsupervised Speech Reconstruction
Human speech can be characterized by different components, including semantic
content, speaker identity and prosodic information. Significant progress has
been made in disentangling representations for semantic content and speaker
identity in Automatic Speech Recognition (ASR) and speaker verification tasks
respectively. However, it is still an open challenging research question to
extract prosodic information because of the intrinsic association of different
attributes, such as timbre and rhythm, and because of the need for supervised
training schemes to achieve robust large-scale and speaker-independent ASR. The
aim of this paper is to address the disentanglement of emotional prosody from
speech based on unsupervised reconstruction. Specifically, we identify, design,
implement and integrate three crucial components in our proposed speech
reconstruction model Prosody2Vec: (1) a unit encoder that transforms speech
signals into discrete units for semantic content, (2) a pretrained speaker
verification model to generate speaker identity embeddings, and (3) a trainable
prosody encoder to learn prosody representations. We first pretrain the
Prosody2Vec representations on unlabelled emotional speech corpora, then
fine-tune the model on specific datasets to perform Speech Emotion Recognition
(SER) and Emotional Voice Conversion (EVC) tasks. Both objective (weighted and
unweighted accuracies) and subjective (mean opinion score) evaluations on the
EVC task suggest that Prosody2Vec effectively captures general prosodic
features that can be smoothly transferred to other emotional speech. In
addition, our SER experiments on the IEMOCAP dataset reveal that the prosody
features learned by Prosody2Vec are complementary and beneficial for the
performance of widely used speech pretraining models and surpass the
state-of-the-art methods when combining Prosody2Vec with HuBERT
representations.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech, and Language
Processin
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
Proceedings of the 7th Sound and Music Computing Conference
Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010
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