Speaker Identification
61 papers with code • 4 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Speaker Identification
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VoxCeleb1
CSI
EVI
FSC-P2
Latest papers
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input
nttcslab/m2d
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We propose a new method, Masked Modeling Duo (M2D), that learns representations directly while obtaining training signals using only masked patches.
Cross-Lingual Speaker Identification Using Distant Supervision
slash0bz/speaker-identification
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Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks.
IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages
We hope IndicSUPERB contributes to the progress of developing speech language understanding models for Indian languages.
Masked Autoencoders that Listen
facebookresearch/multimodal
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Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers.
Extended U-Net for Speaker Verification in Noisy Environments
wngh1187/exu-net
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Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
PaddlePaddle/DeepSpeech
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NAACL (ACL) 2022
PaddleSpeech is an open-source all-in-one speech toolkit.
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.
ATST: Audio Representation Learning with Teacher-Student Transformer
Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data.
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings
mu-y/diarist
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The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency.
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
asappresearch/slue-toolkit
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Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks.
