Discrete-time signal processing
Discrete-time signal processing
Glottal wave analysis with Pitch Synchronous Iterative Adaptive Inverse Filtering
Speech Communication - Eurospeech '91
Parabolic spectral parameter—a new method for quantification of the glottal flow
Speech Communication
A speech spectrum distortion measure with interframe memory
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
A review of glottal waveform analysis
Progress in nonlinear speech processing
A novel source analysis method by matching spectral characters of LF model with STRAIGHT spectrum
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
IEEE Transactions on Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
Robust glottal source estimation based on joint source-filter model optimization
IEEE Transactions on Audio, Speech, and Language Processing
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
Oscillating statistical moments for speech polarity detection
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Automating manual user strategies for precise voice source analysis
Speech Communication
Analysis and HMM-based synthesis of hypo and hyperarticulated speech
Computer Speech and Language
Quasi Closed Phase Glottal Inverse Filtering Analysis With Weighted Linear Prediction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Phonetic feature extraction for context-sensitive glottal source processing
Speech Communication
Speech polarity determination: A comparative evaluation
Neurocomputing
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Abstract: Source-tract decomposition (or glottal flow estimation) is one of the basic problems of speech processing. For this, several techniques have been proposed in the literature. However, studies comparing different approaches are almost nonexistent. Besides, experiments have been systematically performed either on synthetic speech or on sustained vowels. In this study we compare three of the main representative state-of-the-art methods of glottal flow estimation: closed-phase inverse filtering, iterative and adaptive inverse filtering, and mixed-phase decomposition. These techniques are first submitted to an objective assessment test on synthetic speech signals. Their sensitivity to various factors affecting the estimation quality, as well as their robustness to noise are studied. In a second experiment, their ability to label voice quality (tensed, modal, soft) is studied on a large corpus of real connected speech. It is shown that changes of voice quality are reflected by significant modifications in glottal feature distributions. Techniques based on the mixed-phase decomposition and on a closed-phase inverse filtering process turn out to give the best results on both clean synthetic and real speech signals. On the other hand, iterative and adaptive inverse filtering is recommended in noisy environments for its high robustness.