Search Results for author:
Found 6 papers, 1 papers with code
BS-PLCNet 2: Two-stage Band-split Packet Loss Concealment Network with Intra-model Knowledge Distillation
Moreover, we introduce a lightweight post-processing module after packet loss restoration to recover speech distortions and remove residual noise in the audio signal.
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators
Packet loss is a common and unavoidable problem in voice over internet phone (VoIP) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Language-universal phonetic encoder for low-resource speech recognition
Our main approach and adaptation are effective on extremely low-resource languages, even within domain- and language-mismatched scenarios.
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition
Moreover, on 3 of the 4 languages, comparing to the standard HuBERT, the approach performs better, meanwhile is able to save supervised training data by 1. 5k hours (75%) at most.
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition
Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Speech restoration aims to remove distortions in speech signals.
