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Found 24 papers, 5 papers with code
Enhancing Quantised End-to-End ASR Models via Personalisation
Recent end-to-end automatic speech recognition (ASR) models have become increasingly larger, making them particularly challenging to be deployed on resource-constrained devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Attack on practical speaker verification system using universal adversarial perturbations
In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text.
Real-World Adversarial Attack
Room Impulse Response (RIR)
+3
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.
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.
When Automatic Voice Disguise Meets Automatic Speaker Verification
The proposed approach generalizes well to restore the disguise with nonlinear frequency warping in VTLN by reducing its EER from 34. 3% to 18. 5%.
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 factorization for speech signal
Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors.
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.
Enhanced Neural Machine Translation by Learning from Draft
Neural machine translation (NMT) has recently achieved impressive results.
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.
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.
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).
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.
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks.
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.
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.
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.
Distant Supervision for Entity Linking
In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages).
Probabilistic Belief Embedding for Knowledge Base Completion
This paper contributes a novel embedding model which measures the probability of each belief $\langle h, r, t, m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$).
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines.
Learning Embedding Representations for Knowledge Inference on Imperfect and Incomplete Repositories
This paper considers the problem of knowledge inference on large-scale imperfect repositories with incomplete coverage by means of embedding entities and relations at the first attempt.
Errata: Distant Supervision for Relation Extraction with Matrix Completion
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features.
