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# Speaker Verification Edit

20 papers with code · Speech

Speaker verification is the verifying the identity of a person from characteristics of the voice.

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# Non-native Speaker Verification for Spoken Language Assessment

30 Sep 2019

These systems are explored for non-native spoken English data in this paper.

# Self-Adaptive Soft Voice Activity Detection using Deep Neural Networks for Robust Speaker Verification

26 Sep 2019

The first approach is soft VAD, which performs a soft selection of frame-level features extracted from a speaker feature extractor.

# Probing the Information Encoded in X-vectors

13 Sep 2019

Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks.

# The Ambiguous World of Emotion Representation

1 Sep 2019

A key reason for this is the lack of a common mathematical framework to describe all the relevant elements of emotion representations.

# VAE-based Domain Adaptation for Speaker Verification

27 Aug 2019

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.

# Personal VAD: Speaker-Conditioned Voice Activity Detection

12 Aug 2019

In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level.

# A Study on Angular Based Embedding Learning for Text-independent Speaker Verification

12 Aug 2019

Learning a good speaker embedding is important for many automatic speaker recognition tasks, including verification, identification and diarization.

# Triplet Based Embedding Distance and Similarity Learning for Text-independent Speaker Verification

6 Aug 2019

The improvements are both based on triplet cause the training stage and the evaluation stage of the baseline x-vector system focus on different aims.

# An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network

6 Aug 2019

In training our speaker verification framework, we consider both the triplet loss minimization and adversarial gradient of the ASR network to obtain more discriminative and text-independent speaker embedding vectors.

# V2S attack: building DNN-based voice conversion from automatic speaker verification

5 Aug 2019

The experimental evaluation compares converted voices between the proposed method that does not use the targeted speaker's voice data and the standard VC that uses the data.