Results 11 to 20 of about 4,664 (261)
Content-Aware Speaker Embeddings for Speaker Diarisation [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Recent speaker diarisation systems often convert variable length speech segments into fixed-length vector representations for speaker clustering, which are known as speaker embeddings. In this paper, the content-aware speaker embeddings (CASE) approach is proposed, which extends the input of the speaker classifier to include not only acoustic features ...
Guangzhi Sun +3 more
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Probabilistic Embeddings for Speaker Diarization [PDF]
The Speaker and Language Recognition Workshop (Odyssey 2020), 2020 Awarded: Jack Godfrey Best Student Paper Award, at Odyssey 2020: The Speaker and Language Recognition Workshop ...
Silnova, Anna +4 more
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Binary speaker embedding [PDF]
2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2016 The popular i-vector model represents speakers as low-dimensional continuous vectors (i-vectors), and hence it is a way of continuous speaker embedding. In this paper, we investigate binary speaker embedding, which transforms i-vectors to binary vectors (codes) by a hash function.
Lantian Li +4 more
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Speaker diarization through speaker embeddings [PDF]
2015 23rd European Signal Processing Conference (EUSIPCO), 2015 Publication in the conference proceedings of EUSIPCO, Nice, France ...
Mickael Rouvier +2 more
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Frontiers in Artificial Intelligence, 2023 Modeling virtual agents with behavior style is one factor for personalizing human-agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different ...
Mireille Fares +2 more
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Combination of deep speaker embeddings for diarisation [PDF]
Neural Networks, 2021 Significant progress has recently been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from neural network (NN) speaker classifiers for clustering speech segments. To extract better-performing and more robust speaker embeddings, this paper proposes a c-vector method by combining multiple sets of ...
Guangzhi Sun +2 more
openaire +3 more sources
Residual Information in Deep Speaker Embedding Architectures
Mathematics, 2022 Speaker embeddings represent a means to extract representative vectorial representations from a speech signal such that the representation pertains to the speaker identity alone.
Adriana Stan
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Speaker/Style-Dependent Neural Network Speech Synthesis Based on Speaker/Style Embedding [PDF]
Journal of Universal Computer Science, 2020 The paper presents a novel architecture and method for training neural networks to produce synthesized speech in a particular voice and speaking style, based on a small quantity of target speaker/style training data. The method is based on neural network
Milan Sečujski +4 more
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IEEE Access, 2021 This paper proposes a non-parallel cross-lingual voice conversion (CLVC) model that can mimic voice while continuously controlling speaker individuality on the basis of the variational autoencoder (VAE) and star generative adversarial network (StarGAN ...
Tuan Vu Ho, Masato Akagi
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U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
Applied Sciences, 2021 Speaker recognition deals with recognizing speakers by their speech. Most speaker recognition systems are built upon two stages, the first stage extracts low dimensional correlation embeddings from speech, and the second performs the classification task.
Muhammad Firoz Mridha +5 more
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