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Found 6 papers, 4 papers with code
AlloST: Low-resource Speech Translation without Source Transcription
In this study, we survey methods to improve ST performance without using source transcription, and propose a learning framework that utilizes a language-independent universal phone recognizer.
Chain-based Discriminative Autoencoders for Speech Recognition
For application to robust speech recognition, we further extend c-DcAE to hierarchical and parallel structures, resulting in hc-DcAE and pc-DcAE.
Speech-enhanced and Noise-aware Networks for Robust Speech Recognition
In this paper, a noise-aware training framework based on two cascaded neural structures is proposed to jointly optimize speech enhancement and speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CasNet: Investigating Channel Robustness for Speech Separation
In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by proposing a channel-aware audio separation network (CasNet), a deep learning framework for end-to-end time-domain speech separation.
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech
The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that there is an inevitable mismatch between their training criterion and evaluation metric.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
