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Lightweight Embeddings for Speaker Verification
2018This paper presents speaker verification (SV) system using deep neural networks with hash representations (binarization) of embeddings. The training procedure is performed on NIST SRE train set, verification is performed on the same corpus with test set. The system architecture is based on deep recurrent layers with attention mechanism.
Maxim Tkachenko +3 more
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Investigation of speaker embeddings for cross-show speaker diarization
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016This paper proposes to investigate speaker embeddings, a representation extracted from hidden layers of deep neural networks trained on a speaker identification task, on cross-show diarization. The new representation brings an improvement over i-vectors, and we show that while shallow hidden layers give best results on the single-show condition, deeper
Mickael Rouvier, Benoît Favre
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Embedded Modules for Speaker Classification
2008 IEEE International Conference on Semantic Computing, 2008Classifying speakers and their context is a research topic that increasingly finds its way into market-ready products. This paper describes how a speech-based classification problem can be split into components that are then combined in a classification module, which can be compiled for a specific platform and scenario with its respective technical ...
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A Deep Speaker Embedding Transfer Method for Speaker Verification
2019Recently a kind of deep speaker embedding called x-vector has been proposed. It is extracted from deep neural network and considered as a strong contender for next-generation representation for speaker recognition. However, training such DNNs requires a lot of data, usually thousands of hours.
Kai Zhou +3 more
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Speaker Diarization using Embedding Vectors
2020 28th Signal Processing and Communications Applications Conference (SIU), 2020In recent years, with the rapid increase of voice data, solutions are being sought extensively for examination and indexing in the field of speech processing. One of these solutions is the speaker diarization, which is used to examine speech records that include multi-speaker.
Toruk, M Mesut +2 more
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Speaker-Aware Target Speaker Enhancement by Jointly Learning with Speaker Embedding Extraction
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020Deep learning based speech separation approaches have received great interest, among which the recent speaker-aware speech enhancement methods are promising for solving difficulties such as arbitrary source permutation and unknown number of sources. In this paper, we propose a novel training framework which jointly learns the speaker-conditioned target
Xuan Ji +6 more
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2020
Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity.
Georges, Munir +2 more
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Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity.
Georges, Munir +2 more
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Disentangled Speaker Embedding for Robust Speaker Verification
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022Lu Yi 0001, Man-Wai Mak
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Speaker-Corrupted Embeddings for Online Speaker Diarization
Interspeech 2019, 2019Omid Ghahabi, Volker Fischer 0002
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ANSD-MA-MSE: Adaptive Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding
IEEE/ACM Transactions on Audio Speech and Language Processing, 2023Chin-Hui Lee, Jun Du, Mao-Kui He
exaly