Search Results for author:
Found 42 papers, 5 papers with code
Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining
We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic.
Adversarial Data Augmentation for Robust Speaker Verification
This adversarial learning empowers the network to generate speaker embeddings that can deceive the augmentation classifier, making the learned speaker embeddings more robust in the face of augmentation variations.
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.
CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition
A comprehensive study was conducted to compare CN-Celeb-AV with two popular public AVPR benchmark datasets, and the results demonstrated that CN-Celeb-AV is more in line with real-world scenarios and can be regarded as a new benchmark dataset for AVPR research.
Ordered and Binary Speaker Embedding
Modern speaker recognition systems represent utterances by embedding vectors.
CycleFlow: Purify Information Factors by Cycle Loss
SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies.
Real Additive Margin Softmax for Speaker Verification
The additive margin softmax (AM-Softmax) loss has delivered remarkable performance in speaker verification.
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.
Deep Speaker Vector Normalization with Maximum Gaussianality Training
In this paper, we argue that this problem is largely attributed to the maximum-likelihood (ML) training criterion of the DNF model, which aims to maximize the likelihood of the observations but not necessarily improve the Gaussianality of the latent codes.
Squeezing value of cross-domain labels: a decoupled scoring approach for speaker verification
Domain mismatch often occurs in real applications and causes serious performance reduction on speaker verification systems.
Deep generative factorization for speech signal
Various information factors are blended in speech signals, which forms the primary difficulty for most speech information processing tasks.
Domain-Invariant Speaker Vector Projection by Model-Agnostic Meta-Learning
Domain generalization remains a critical problem for speaker recognition, even with the state-of-the-art architectures based on deep neural nets.
Audio and Speech Processing
Deep Normalization for Speaker Vectors
Deep speaker embedding has demonstrated state-of-the-art performance in speaker recognition tasks.
CN-CELEB: a challenging Chinese speaker recognition dataset
These datasets tend to deliver over optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.
On Investigation of Unsupervised Speech Factorization Based on Normalization Flow
Speech signals are complex composites of various information, including phonetic content, speaker traits, channel effect, etc.
VAE-based Domain Adaptation for Speaker Verification
By enforcing the neural model to discriminate the speakers in the training set, deep speaker embedding (called `x-vectors`) can be derived from the hidden layers.
VAE-based regularization for deep speaker embedding
Deep speaker embedding has achieved state-of-the-art performance in speaker recognition.
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.
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.
Deep factorization for speech signal
Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors.
Human and Machine Speaker Recognition Based on Short Trivial Events
Trivial events are ubiquitous in human to human conversations, e. g., cough, laugh and sniff.
Full-info Training for Deep Speaker Feature Learning
In recent studies, it has shown that speaker patterns can be learned from very short speech segments (e. g., 0. 3 seconds) by a carefully designed convolutional & time-delay deep neural network (CT-DNN) model.
Deep Speaker Verification: Do We Need End to End?
This principle has recently been applied to several prototype research on speaker verification (SV), where the feature learning and classifier are learned together with an objective function that is consistent with the evaluation metric.
Speaker Recognition with Cough, Laugh and "Wei"
This paper proposes a speaker recognition (SRE) task with trivial speech events, such as cough and laugh.
Cross-lingual Speaker Verification with Deep Feature Learning
The experiments demonstrated that the feature-based system outperformed the i-vector system with a large margin, particularly with language mismatch between enrollment and test.
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.
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID).
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID).
Collaborative Learning for Language and Speaker Recognition
This paper presents a unified model to perform language and speaker recognition simultaneously and altogether.
AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline
We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016.
Decision Making Based on Cohort Scores for Speaker Verification
In this paper, we propose a decision making approach based on multiple scores derived from a set of cohort GMMs (cohort scores).
Local Training for PLDA in Speaker Verification
PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification.
Weakly Supervised PLDA Training
PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification.
Multi-task Recurrent Model for True Multilingual Speech Recognition
Research on multilingual speech recognition remains attractive yet challenging.
Multi-task Recurrent Model for Speech and Speaker Recognition
Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities.
System Combination for Short Utterance Speaker Recognition
This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based i-vector system and the other is our recently proposed subregion-based GMM-UBM system.
Binary Speaker Embedding
The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding.
Max-margin Metric Learning for Speaker Recognition
Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition.
Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN).
Deep Speaker Vectors for Semi Text-independent Speaker Verification
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification.
