Phone Sequence

Phone sequence analysis focuses on understanding and modeling the sequential arrangement of phonetic units in speech, aiming to improve speech recognition and language learning. Current research emphasizes developing robust methods for analyzing phone sequences, particularly using deep learning architectures like generative adversarial networks (GANs) and recurrent neural networks (RNNs, such as LSTMs) to handle both paired and unpaired audio-phonetic data. These advancements are significant for low-resource language applications and improving the accuracy of automatic speech recognition, as well as for other applications such as predictive maintenance in fields like wind turbine operations.

Papers

June 19, 2023

Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription
Eun Jung Yeo, Hyungshin Ryu, Jooyoung Lee, Sunhee Kim, Minhwa Chung
Consistent Comparison Automatic Transcription Lagrange 1 Phone Sequence

July 29, 2022

Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network
Da-rong Liu, Po-chun Hsu, Yi-chen Chen, Sung-feng Huang, Shun-po Chuang, Da-yi Wu, Hung-yi Lee
Generative Adversarial Network Acoustic Unit Unpaired Speech Phone Sequence

July 19, 2022

A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms
Connor Walker, Callum Rothon, Koorosh Aslansefat, Yiannis Papadopoulos, Nina Dethlefs
Long Short Term Memory Deep Learning Framework Wind Power Event Sequence Condition Monitoring Phone Sequence

Phone Sequence

Papers

Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription

Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network

A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms