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Protocol for assessing auditory function, cochlear synaptopathy, and afferent terminal morphology in laboratory mice. [PDF]

STAR Protoc
Pal I, Bhattacharyya A, V-Ghaffari B, Williams ED, Xiao M, Rutherford MA, Rubio ME.   +6 more
europepmc   +1 more source

Understanding neural circuit principles for representation learning through joint-embedding predictive architectures

Mohammadi AG, Halvagal MS, Zenke F.
europepmc   +1 more source

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On Deep Speaker Embeddings for Speaker Verification

2021 44th International Conference on Telecommunications and Signal Processing (TSP), 2021
In recent years, there has been a tremendous application spike in the field of deep neural networks (DNN), including increasing interest in automatic speaker recognition systems development. Currently, the utilization of DNN-based speaker embeddings, such as x-vectors or d-vectors, is a common way of creating speaker-specific acoustic models. In recent
Maros Jakubec, Roman Jarina, Eva Lieskovska, Michal Chmulik   +3 more
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Graph-embedding for speaker recognition

Interspeech 2010, 2010
This chapter presents applications of graph embedding to the problem of text-independent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison—given two speech recordings (utterances), produce a score which measures speaker similarity.
Zahi N. Karam, William M. Campbell
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Lightweight Embeddings for Speaker Verification

2018
This 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, Alexander Yamshinin, Mikhail Kotov, Marina Nastasenko   +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), 2016
This 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, 2008
Classifying 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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