1 citations · 2 across the 11 of their papers we have counts for
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Pseudo-label distillation for discriminative anomalous sound detection
Takuya Fujimura, Tomoki Toda
Discriminative anomalous sound detection (ASD) methods train a feature extractor through a classification task using machine-information labels. They then detect anomalies in the r…
Advancing Electrolaryngeal Speech Enhancement Through Speech-Text Representation Learning
Ding Ma, Jinyi Mi, Fengji Li +5
Objective: laryngectomees depend on an electromechanical device to generate electrolaryngeal (EL) speech. Compared with normal speech, EL speech suffers from severe distortion, lim…
Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work
Kevin Wilkinghoff, Takuya Fujimura, Keisuke Imoto +3
When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, wh…
Speaker Privacy and Security in the Big Data Era: Protection and Defense against Deepfake
Liping Chen, Kong Aik Lee, Zhen-Hua Ling +4
In the era of big data, remarkable advancements have been achieved in personalized speech generation techniques that utilize speaker attributes, including voice and speaking style,…
Layer-wise Analysis for Quality of Multilingual Synthesized Speech
Erica Cooper, Takuma Okamoto, Yamato Ohtani +2
While supervised quality predictors for synthesized speech have demonstrated strong correlations with human ratings, their requirement for in-domain labeled training data hinders t…
Unifying Listener Scoring Scales: Comparison Learning Framework for Speech Quality Assessment and Continuous Speech Emotion Recognition
Cheng-Hung Hu, Yusuke Yasuda, Akifumi Yoshimoto +1
Speech Quality Assessment (SQA) and Continuous Speech Emotion Recognition (CSER) are two key tasks in speech technology, both relying on listener ratings. However, these ratings ar…