Papers Text-Independent Speaker Recognition
“Text-Independent Speaker Recognition” 태그가 달린 논문 21편 · 필터 해제
Study on Inter and Intra Speaker Variability in Speaker Recognition
Optimization of a trade-off between the number of speakers and their temporal variability (or session diversity) is crucial for the development of a speaker recognition system together with making the data collection pro…
DiversitySpeaker RecognitionText-Independent Speaker RecognitionPersonalizing Keyword Spotting with Speaker Information
Keyword spotting systems often struggle to generalize to a diverse population with various accents and age groups. To address this challenge, we propose a novel approach that integrates speaker information into keyword s…
Keyword SpottingSpeaker RecognitionText-Independent Speaker RecognitionA Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition
A novel speech feature fusion algorithm with independent vector analysis (IVA) and parallel convolutional neural network (PCNN) is proposed for text-independent speaker recognition. Firstly, some different feature types,…
Speaker RecognitionText-Independent Speaker RecognitionAttention and DCT based Global Context Modeling for Text-independent Speaker Recognition
Learning an effective speaker representation is crucial for achieving reliable performance in speaker verification tasks. Speech signals are high-dimensional, long, and variable-length sequences containing diverse inform…
Speaker RecognitionSpeaker VerificationText-Independent Speaker RecognitionTemporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
In the field of text-independent speaker recognition, dynamic models that adapt along the time axis have been proposed to consider the phoneme-varying characteristics of speech. However, a detailed analysis of how dynami…
Speaker RecognitionSpeaker VerificationText-Independent Speaker RecognitionText-Independent Speaker VerificationSpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification
Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-le…
Neural Architecture SearchSpeaker RecognitionSpeaker VerificationText-Independent Speaker RecognitionMasked Proxy Loss For Text-Independent Speaker Verification
Open-set speaker recognition can be regarded as a metric learning problem, which is to maximize inter-class variance and minimize intra-class variance. Supervised metric learning can be categorized into entity-based lear…
Metric LearningSpeaker RecognitionSpeaker VerificationText-Independent Speaker Recognition+2Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition
Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two-dimensional (2D) lip images to recognize speake…
SentenceSpeaker RecognitionText-Independent Speaker RecognitionA Lightweight Speaker Recognition System Using Timbre Properties
Speaker recognition is an active research area that contains notable usage in biometric security and authentication system. Currently, there exist many well-performing models in the speaker recognition domain. However, m…
GPUSpeaker IdentificationSpeaker RecognitionSpeaker Verification+3JukeBox: A Multilingual Singer Recognition Dataset
A text-independent speaker recognition system relies on successfully encoding speech factors such as vocal pitch, intensity, and timbre to achieve good performance. A majority of such systems are trained and evaluated us…
Speaker RecognitionText-Independent Speaker RecognitionEvidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques
Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condi…
EEGElectroencephalogram (EEG)Speaker RecognitionText-Independent Speaker RecognitionFrequency and temporal convolutional attention for text-independent speaker recognition
Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end. In t…
Speaker RecognitionSpeaker VerificationText-Independent Speaker RecognitionProbing the Information Encoded in X-vectors
Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks. In this paper, we use simple classifiers to investigate the con…
Data AugmentationSentenceSpeaker RecognitionSpeaker Verification+1Channel adversarial training for cross-channel text-independent speaker recognition
The conventional speaker recognition frameworks (e.g., the i-vector and CNN-based approach) have been successfully applied to various tasks when the channel of the enrolment dataset is similar to that of the test dataset…
Domain AdaptationSpeaker RecognitionText-Independent Speaker RecognitionDeep neural network based i-vector mapping for speaker verification using short utterances
Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector based systems have become the standard in speake…
Speaker RecognitionSpeaker VerificationText-Independent Speaker RecognitionFrame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model
In this paper, we propose a Convolutional Neural Network (CNN) based speaker recognition model for extracting robust speaker embeddings. The embedding can be extracted efficiently with linear activation in the embedding …
Speaker RecognitionText-Independent Speaker RecognitionUnified Hypersphere Embedding for Speaker Recognition
Incremental improvements in accuracy of Convolutional Neural Networks are usually achieved through use of deeper and more complex models trained on larger datasets. However, enlarging dataset and models increases the com…
Speaker RecognitionText-Independent Speaker RecognitionOn deep speaker embeddings for text-independent speaker recognition
We investigate deep neural network performance in the textindependent speaker recognition task. We demonstrate that using angular softmax activation at the last classification layer of a classification neural network ins…
General ClassificationMetric LearningSpeaker RecognitionSpeaker Verification+1PCA/LDA Approach for Text-Independent Speaker Recognition
Various algorithms for text-independent speaker recognition have been developed through the decades, aiming to improve both accuracy and efficiency. This paper presents a novel PCA/LDA-based approach that is faster than …
Speaker RecognitionText-Independent Speaker RecognitionDeep 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. This new method has been teste…
Speaker RecognitionSpeaker VerificationText-Dependent Speaker VerificationText-Independent Speaker Recognition+1