761 research outputs found
Learning vocal tract variables with multi-task kernels
International audienceThe problem of acoustic-to-articulatory speech inversion continues to be a challenging research problem which sig- nificantly impacts automatic speech recognition robustness and accuracy. This paper presents a multi-task kernel based method aimed at learning Vocal Tract (VT) variables from the Mel-Frequency Cepstral Coefficients (MFCCs). Unlike usual speech inversion techniques based on individual esti- mation of each tract variable, the key idea here is to consider all the target variables simultaneously to take advantage of the relationships among them and then improve learning per- formance. The proposed method is evaluated using synthetic speech dataset and corresponding tract variables created by the TAsk Dynamics Application (TADA) model and com- pared to the hierarchical ε-SVR speech inversion technique
Towards Emotion Recognition: A Persistent Entropy Application
Emotion recognition and classification is a very active area of research. In
this paper, we present a first approach to emotion classification using
persistent entropy and support vector machines. A topology-based model is
applied to obtain a single real number from each raw signal. These data are
used as input of a support vector machine to classify signals into 8 different
emotions (calm, happy, sad, angry, fearful, disgust and surprised)
Towards Emotion Recognition: A Persistent Entropy Application
Emotion recognition and classification is a very active area of research. In this paper, we present
a first approach to emotion classification using persistent entropy and support vector machines. A
topology-based model is applied to obtain a single real number from each raw signal. These data are
used as input of a support vector machine to classify signals into 8 different emotions (calm, happy,
sad, angry, fearful, disgust and surprised)
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Machine Learning Mitigants for Speech Based Cyber Risk
Statistical analysis of speech is an emerging area of machine learning. In this paper, we tackle the biometric challenge of Automatic Speaker Verification (ASV) of differentiating between samples generated by two distinct populations of utterances, those of an authentic human voice and those generated by a synthetic one. Solving such an issue through a statistical perspective foresees the definition of a decision rule function and a learning procedure to identify the optimal classifier. Classical state-of-the-art countermeasures rely on strong assumptions such as stationarity or local-stationarity of speech that may be atypical to encounter in practice. We explore in this regard a robust non-linear and non-stationary signal decomposition method known as the Empirical Mode Decomposition combined with the Mel-Frequency Cepstral Coefficients in a novel fashion with a refined classifier technique known as multi-kernel Support Vector machine. We undertake significant real data case studies covering multiple ASV systems using different datasets, including the ASVSpoof 2019 challenge database. The obtained results overwhelmingly demonstrate the significance of our feature extraction and classifier approach versus existing conventional methods in reducing the threat of cyber-attack perpetrated by synthetic voice replication seeking unauthorised access
Models and analysis of vocal emissions for biomedical applications
This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies
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