8 citations · 19 across the 11 of their papers we have counts for
11 papers
Sagalee: an Open Source Automatic Speech Recognition Dataset for Oromo Language
Turi Abu, Ying Shi, Thomas Fang Zheng +1
We present a novel Automatic Speech Recognition (ASR) dataset for the Oromo language, a widely spoken language in Ethiopia and neighboring regions. The dataset was collected throug…
A Glance is Enough: Extract Target Sentence By Looking at A keyword
Ying Shi, Dong Wang, Lantian Li +1
This paper investigates the possibility of extracting a target sentence from multi-talker speech using only a keyword as input. For example, in social security applications, the ke…
Spot keywords from very noisy and mixed speech
Ying Shi, Dong Wang, Lantian Li +2
Most existing keyword spotting research focuses on conditions with slight or moderate noise. In this paper, we try to tackle a more challenging task: detecting keywords buried unde…
Can We Trust Deep Speech Prior?
Ying Shi, Haolin Chen, Zhiyuan Tang +3
Recently, speech enhancement (SE) based on deep speech prior has attracted much attention, such as the variational auto-encoder with non-negative matrix factorization (VAE-NMF) arc…
Phonetic-attention scoring for deep speaker features in speaker verification
Lantian Li, Zhiyuan Tang, Ying Shi +1
Recent studies have shown that frame-level deep speaker features can be derived from a deep neural network with the training target set to discriminate speakers by a short speech s…
Gaussian-Constrained training for speaker verification
Lantian Li, Zhiyuan Tang, Ying Shi +1
Neural models, in particular the d-vector and x-vector architectures, have produced state-of-the-art performance on many speaker verification tasks. However, two potential problems…
Deep factorization for speech signal
Lantian Li, Dong Wang, Yixiang Chen +3
Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individu…
Deep Factorization for Speech Signal
Dong Wang, Lantian Li, Ying Shi +2
Speech signals are complex intermingling of various informative factors, and this information blending makes decoding any of the individual factors extremely difficult. A natural i…
Phone-aware Neural Language Identification
Zhiyuan Tang, Dong Wang, Yixiang Chen +2
Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlo…
Deep Speaker Feature Learning for Text-independent Speaker Verification
Lantian Li, Yixiang Chen, Ying Shi +2
Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, e…
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition
Zhiyuan Tang, Ying Shi, Dong Wang +2
Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurren…