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Found 16 papers, 5 papers with code
The Academia Sinica Systems of Voice Conversion for VCC2020
This paper describes the Academia Sinica systems for the two tasks of Voice Conversion Challenge 2020, namely voice conversion within the same language (Task 1) and cross-lingual voice conversion (Task 2).
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.
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available.
Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation
DPFN is composed of two parts: the speaker module and the separation module.
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)
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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.
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition
Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events.
Generation of Speaker Representations Using Heterogeneous Training Batch Assembly
In traditional speaker diarization systems, a well-trained speaker model is a key component to extract representations from consecutive and partially overlapping segments in a long speech session.
Disentangling the Impacts of Language and Channel Variability on Speech Separation Networks
However, domain mismatch between training/test situations due to factors, such as speaker, content, channel, and environment, remains a severe problem for speech separation.
Filter-based Discriminative Autoencoders for Children Speech Recognition
Children speech recognition is indispensable but challenging due to the diversity of children's speech.
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.
The North System for Formosa Speech Recognition Challenge 2023
This report provides a concise overview of the proposed North system, which aims to achieve automatic word/syllable recognition for Taiwanese Hakka (Sixian).
