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Found 12 papers, 0 papers with code
A Glance is Enough: Extract Target Sentence By Looking at A keyword
This paper investigates the possibility of extracting a target sentence from multi-talker speech using only a keyword as input.
Spot keywords from very noisy and mixed speech
We propose a novel Mix Training (MT) strategy that encourages the model to discover low-energy keywords from noisy and mixed speech.
Can We Trust Deep Speech Prior?
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) architecture.
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes.
Gaussian-Constrained training for speaker verification
This paper proposes a Gaussian-constrained training approach that (1) discards the parametric classifier, and (2) enforces the distribution of the derived speaker vectors to be Gaussian.
Phonetic-attention scoring for deep speaker features in speaker verification
This score reflects the similarity of the two frames in phonetic content, and is used to weigh the contribution of this frame pair in the utterance-based scoring.
Deep factorization for speech signal
Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors.
Deep Factorization for Speech Signal
In this paper, we demonstrated that the speaker factor is also a short-time spectral pattern and can be largely identified with just a few frames using a simple deep neural network (DNN).
Deep Speaker Feature Learning for Text-independent Speaker Verification
Recently deep neural networks (DNNs) have been used to learn speaker features.
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID).
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition
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 recurrent unit (GRU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Pursing power in Arabic on-line discussion forums
An analysis of our annotations reflects a high-degree of overlap between current theories on power and conflict within a group and the behavior of individuals within the transcripts.
