131 research outputs found
Novel Pitch Detection Algorithm With Application to Speech Coding
This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions
Analysis and Detection of Pathological Voice using Glottal Source Features
Automatic detection of voice pathology enables objective assessment and
earlier intervention for the diagnosis. This study provides a systematic
analysis of glottal source features and investigates their effectiveness in
voice pathology detection. Glottal source features are extracted using glottal
flows estimated with the quasi-closed phase (QCP) glottal inverse filtering
method, using approximate glottal source signals computed with the zero
frequency filtering (ZFF) method, and using acoustic voice signals directly. In
addition, we propose to derive mel-frequency cepstral coefficients (MFCCs) from
the glottal source waveforms computed by QCP and ZFF to effectively capture the
variations in glottal source spectra of pathological voice. Experiments were
carried out using two databases, the Hospital Universitario Principe de
Asturias (HUPA) database and the Saarbrucken Voice Disorders (SVD) database.
Analysis of features revealed that the glottal source contains information that
discriminates normal and pathological voice. Pathology detection experiments
were carried out using support vector machine (SVM). From the detection
experiments it was observed that the performance achieved with the studied
glottal source features is comparable or better than that of conventional MFCCs
and perceptual linear prediction (PLP) features. The best detection performance
was achieved when the glottal source features were combined with the
conventional MFCCs and PLP features, which indicates the complementary nature
of the features
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
Enhancing dysarthria speech feature representation with empirical mode decomposition and Walsh-Hadamard transform
Dysarthria speech contains the pathological characteristics of vocal tract
and vocal fold, but so far, they have not yet been included in traditional
acoustic feature sets. Moreover, the nonlinearity and non-stationarity of
speech have been ignored. In this paper, we propose a feature enhancement
algorithm for dysarthria speech called WHFEMD. It combines empirical mode
decomposition (EMD) and fast Walsh-Hadamard transform (FWHT) to enhance
features. With the proposed algorithm, the fast Fourier transform of the
dysarthria speech is first performed and then followed by EMD to get intrinsic
mode functions (IMFs). After that, FWHT is used to output new coefficients and
to extract statistical features based on IMFs, power spectral density, and
enhanced gammatone frequency cepstral coefficients. To evaluate the proposed
approach, we conducted experiments on two public pathological speech databases
including UA Speech and TORGO. The results show that our algorithm performed
better than traditional features in classification. We achieved improvements of
13.8% (UA Speech) and 3.84% (TORGO), respectively. Furthermore, the
incorporation of an imbalanced classification algorithm to address data
imbalance has resulted in a 12.18% increase in recognition accuracy. This
algorithm effectively addresses the challenges of the imbalanced dataset and
non-linearity in dysarthric speech and simultaneously provides a robust
representation of the local pathological features of the vocal folds and
tracts
On the Use of Wavelets and Cepstrum Excitation for Pitch Determination in Real-Time
International audienceIn the current paper, we propose a new pitch tracking technique based on a wavelet transform in the temporal domain. Our algorithm is designed to determine the pitch frequency of the speech signal using a simple voicing decision algorithm. The pitch period is extracted from the cepstrum excitation signal processed by a wavelet transform; then the pitch contour is refined by thresholding and correction algorithms without any post-processing. The results obtained show that the proposed algorithm provides very good pitch contours compared to those furnished by the Bagshaw database
Voice pathologies : the most comum features and classification tools
Speech pathologies are quite common in society, however the exams that exist are invasive, making them uncomfortable for patients and depending on the experience of the clinician who performs the assessment. Hence the need to develop non-invasive methods, which allow objective and efficient analysis. Taking this need into account in this work, the most promising list of features and classifiers was identified. As features, jitter, shimmer, HNR, LPC, PLP, and MFCC were identified and as classifiers CNN, RNN and LSTM. This study intends to develop a device to support medical decision, however this article already presents the system interface.info:eu-repo/semantics/publishedVersio
Glottal-synchronous speech processing
Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity
of voiced speech is exploited. Traditionally, speech processing involves segmenting
and processing short speech frames of predefined length; this may fail to exploit the inherent
periodic structure of voiced speech which glottal-synchronous speech frames have
the potential to harness. Glottal-synchronous frames are often derived from the glottal
closure instants (GCIs) and glottal opening instants (GOIs).
The SIGMA algorithm was developed for the detection of GCIs and GOIs from
the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and
GOI detection from speech signals, the YAGA algorithm provides a measured accuracy
of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to
reverberation than single-channel algorithms.
The GCIs are applied to real-world applications including speech dereverberation,
where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance
of voicing detection in glottal-synchronous algorithms is demonstrated by subjective
testing. The GCIs are further exploited in a new area of data-driven speech modelling,
providing new insights into speech production and a set of tools to aid deployment into
real-world applications. The technique is shown to be applicable in areas of speech coding,
identification and artificial bandwidth extension of telephone speec
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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