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Seyed Reza Shahamiri

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Senior Lecturer

University of Auckland

New Zealand

Research Interests

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Dr. Seyed Reza Shahamiri is an esteemed lecturer at The University of Auckland, New Zealand. His research focuses on speech and language processing, particularly in the context of dysarthric speech recognition and the application of deep learning technologies. Recent publications include investigations into speaker identification, automated assessment for intelligibility, and the development of classification systems using artificial intelligence.

Recent Grants

Grant: Close

Context-Aware Design of an Informal Caregiver Framework for Medical Emergency Situations

Open Date: 2019-03-01

Close Date:

Grant: Close

Improving ASD Pre-Diagnosis using Artificial Intelligence and Machine Learning

Open Date: 2019-03-01

Close Date:

Grant: Close

Deep Neural Network based Speaker Identification to Increase Smart Devices Security

Open Date: 2018-01-01

Close Date:

Grant: Close

Novel Approaches for Impaired-Speech Recognition

Open Date: 2017-07-01

Close Date: 2019-06-01

Grant: Close

Human Computer Interfaces for Speech Disabled

Open Date: 2015-01-01

Close Date: 2017-01-01

Articles (28)

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Collaborators (6)

Muhammad Bilal

Graduate Teaching Assistant

University of Auckland

NEW ZEALAND

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Clarion Mendes

Clinical Associate Professor

University of Illinois at Urbana-Champaign

UNITED STATES

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Hiran Thabrew

Associate Professor

University of Auckland

NEW ZEALAND

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Mark Hasegawa-Johnson

University of Illinois at Urbana-Champaign

UNITED STATES

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Kelly Blincoe

Senior Lecturer

University of Auckland

NEW ZEALAND

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Waleed Abdulla

Associate Professor

University of Auckland

NEW ZEALAND

View details

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Seyed Reza Shahamiri

Research Interests

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About

Recent Grants

Articles (28)

Unveiling Bias in the Autism AI Dataset: A Patterned Sampling Approach for Balanced Learning

Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

Convolution-Augmented Transformers for Enhanced Speaker-Independent Dysarthric Speech Recognition

With data in mind: AI applications in depression, autism and dementia care

Multimodal Speech Analysis for Early Detection of Mild Cognitive Impairment: A Scalable Approach

Investigating Diagnostic Data and Performance Implications in the Autism AI Dataset

Beyond Binary Detection: Multi-Etiology Dysarthria Classification with Pre-Trained Speech Models

Dysarthric Speech Conformer: Adaptation for Sequence-to-Sequence Dysarthric Speech Recognition

Robust Cross-Etiology and Speaker-Independent Dysarthric Speech Recognition

A Comprehensive Performance Evaluation of Whisper Models in Dysarthric Speech Recognition

A Preliminary Investigation on Autism AI Dataset: A Hybrid Learning Paradigm

Correction to: EAI 3rd International Conference on Smart Technologies and Innovation Management

Dynamic Sign Language Recognition Through an Augmented Reality Environment

Conversation in forums: How software forum posts discuss potential development insights

An optimized enhanced-multi learner approach towards speaker identification based on single-sound segments

Generating synthetic dysarthric speech to overcome dysarthria acoustic data scarcity

Dysarthric Speech Transformer: A Sequence-to-Sequence Dysarthric Speech Recognition System

Speech and language processing with deep learning for dementia diagnosis: A systematic review

An Investigation to Identify Optimal Setup for Automated Assessment of Dysarthric Intelligibility using Deep Learning Technologies

An Investigation to Identify Optimal Setup for Automated Assessment of Dysarthric Intelligibility using Deep Learning Technologies

Neural network-based multi-view enhanced multi-learner active learning: theory and experiments

A new classification system for autism based on machine learning of artificial intelligence

Generating synthetic dysarthric speech to overcome dysarthria acoustic data scarcity

Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System

An investigation towards speaker identification using a single-sound-frame

An investigation towards speaker identification using a single-sound-frame

Least Loss: A simplified filter method for feature selection

Least Loss: A simplified filter method for feature selection

Collaborators (6)

Muhammad Bilal

Clarion Mendes

Hiran Thabrew

Mark Hasegawa-Johnson

Kelly Blincoe

Waleed Abdulla

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