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Found 12 papers, 1 papers with code
Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals
Formant tracking experiments with a wide variety of synthetic and natural speech signals show that the proposed TVQCP method performs better than conventional and popular formant tracking tools, such as Wavesurfer and Praat (based on dynamic programming), the KARMA algorithm (based on Kalman filtering), and DeepFormants (based on deep neural networks trained in a supervised manner).
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge
On ComParE 2020 tasks, we investigate applying an ensemble of E2E models for robust performance and developing task-specific modifications for each task.
Spectral modification for recognition of children’s speech undermismatched conditions
The proposed method is used to improve the speech intelligibility to enhance the children’s speech recognition using an acoustic model trained on adult speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs).
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks
The current state-of-the-art methods proposed for these tasks are ensembles based on deep neural networks like ResNets in conjunction with feature engineering.
Investigation of Self-supervised Pre-trained Models for Classification of Voice Quality from Speech and Neck Surface Accelerometer Signals
Between the features, the pre-trained model-based features showed better classification accuracies, both for speech and NSA inputs compared to the conventional features.
Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis.
Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features
Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment.
Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech
Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis.
Analysis and Detection of Pathological Voice using Glottal Source Features
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.
