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- research-articleJuly 2024
Automated ASD detection in children from raw speech using customized STFT-CNN model
- Kurma Venkata Keerthana Sai,
- Rompicharla Thanmayee Krishna,
- Kodali Radha,
- Dhulipalla Venkata Rao,
- Abdul Muneera
International Journal of Speech Technology (SPIJST), Volume 27, Issue 3Pages 701–716https://doi.org/10.1007/s10772-024-10131-7AbstractAutism spectrum disorder (ASD), a prevalent neurodevelopmental condition impacting cognitive, communicative, and behavioral aspects, typically manifests in early childhood due to genetic, environmental, and immunological factors. Employing a novel ...
- correctionJuly 2024
- research-articleJune 2024
Automatic dysarthria detection and severity level assessment using CWT-layered CNN model
- Shaik Sajiha,
- Kodali Radha,
- Dhulipalla Venkata Rao,
- Nammi Sneha,
- Suryanarayana Gunnam,
- Durga Prasad Bavirisetti
EURASIP Journal on Audio, Speech, and Music Processing (EJASMP), Volume 2024, Issue 1https://doi.org/10.1186/s13636-024-00357-3AbstractDysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by ...
- articleJuly 2024
Speech and speaker recognition using raw waveform modeling for adult and children’s speech: A comprehensive review
Engineering Applications of Artificial Intelligence (EAAI), Volume 131, Issue Chttps://doi.org/10.1016/j.engappai.2023.107661AbstractConventionally, the extraction of hand-crafted acoustic features has been separated from the task of establishing robust machine-learning models in speech processing. The manual approach of feature engineering is both time-consuming and ...
- research-articleJuly 2024
Automatic speaker and age identification of children from raw speech using sincNet over ERB scale
AbstractThis paper presents the newly developed non-native children’s English speech (NNCES) corpus to reveal the findings of automatic speaker and age recognition from raw speech. Convolutional neural networks (CNN), which have the ability to learn low-...
Highlights- The study proposes the use of the SincNet model to extract significant speech cues from children’s raw speech, and evaluates its effectiveness for automatic speaker and age identification tasks.
- The article highlights the benefits of ...
- research-articleFebruary 2024
Variable STFT Layered CNN Model for Automated Dysarthria Detection and Severity Assessment Using Raw Speech
Circuits, Systems, and Signal Processing (CSSP), Volume 43, Issue 5Pages 3261–3278https://doi.org/10.1007/s00034-024-02611-7AbstractThis paper presents a novel approach for automated dysarthria detection and severity assessment using a variable short-time Fourier transform layered convolutional neural networks (CNN) model. Dysarthria is a speech disorder characterized by ...
- research-articleSeptember 2023
Towards modeling raw speech in gender identification of children using sincNet over ERB scale
International Journal of Speech Technology (SPIJST), Volume 26, Issue 3Pages 651–663https://doi.org/10.1007/s10772-023-10039-8AbstractThis article reveals the results of age-dependent gender identification from raw speech using the recently developed non-native children’s English speech corpus. Convolutional neural networks (CNN), which can learn low-level speech patterns, may ...
- research-articleMay 2023
Feature Fusion and Ablation Analysis in Gender Identification of Preschool Children from Spontaneous Speech
Circuits, Systems, and Signal Processing (CSSP), Volume 42, Issue 10Pages 6228–6252https://doi.org/10.1007/s00034-023-02399-yAbstractThe children below 6 years of age are called preliterate who use speech as one of their primary forms of communication. Fundamental frequency or pitch is a characteristic that is used to classify gender, but young children have reasonably similar ...