Self-Attending RNN for Speech Enhancement to Improve Cross-Corpus Generalization
Deep neural networks (DNNs) represent the mainstream methodology for supervised speech enhancement, primarily due to their capability to model…
Read moreTASLP Volume 30 | 2022
Towards Textual Out-of-Domain Detection Without In-Domain Labels
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong…
Read moreOcclusion Effect Cancellation in Headphones and Hearing Devices—The Sister of Active Noise Cancellation
The perception of one’s own voice influences the acceptance of hearing devices, such as headphones, headsets or hearing aids. When these devices…
Read moreText Generation From Data With Dynamic Planning
Transcribing structural data into readable text (data-to-text) is a fundamental language generation task. One of its challenges is to plan the input…
Read moreScalable and Efficient Neural Speech Coding: A Hybrid Design
We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding…
Read moreRODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems
Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance…
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