Predicting Fundamental Frequency Patterns in Electrolaryngeal Speech Using Automated Phoneme Extraction

Predicting Fundamental Frequency Patterns in Electrolaryngeal Speech Using Automated Phoneme Extraction

We propose a system to enhance electrolaryngeal speech naturalness using automatically extracted phoneme representations. Phonemes provide sufficient information for predicting reasonably natural fundamental frequency patterns. Previous studies using forced-aligned phoneme labels to create shared fe...

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Bibliographic Details
Main Authors: Mohammad Eshghi, Tomoki Toda
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Automatic speech recognition
electrolaryngeal speech
forced alignment
fundamental frequency prediction
k-means clustering
phoneme embeddings
Online Access:https://ieeexplore.ieee.org/document/10978849/
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https://ieeexplore.ieee.org/document/10978849/

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