Speaker Diarization
74 papers with code • 12 benchmarks • 11 datasets
Speaker Diarization is the task of segmenting and co-indexing audio recordings by speaker. The way the task is commonly defined, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, diarization implies finding speaker boundaries and grouping segments that belong to the same speaker, and, as a by-product, determining the number of distinct speakers. In combination with speech recognition, diarization enables speaker-attributed speech-to-text transcription.
Source: Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm
Benchmarks
These leaderboards are used to track progress in Speaker Diarization
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Libraries
Use these libraries to find Speaker Diarization models and implementationsDatasets
AVA
CHiME-5
AliMeeting
DIHARD II
AVA-ActiveSpeaker
Most implemented papers
The EURECOM Submission to the First DIHARD Challenge
The first DIHARD challenge aims to promote speaker diarization research and to foster progress in domain robustness.
Fully Supervised Speaker Diarization
google/uis-rnn
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In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN).
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
faroit/CountNet
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IEEE/ACM Transactions on Audio, Speech, and Language Processing 2018
Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance.
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
The dataset contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible.
The Second DIHARD Diarization Challenge: Dataset, task, and baselines
This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain.
Ultrasound tongue imaging for diarization and alignment of child speech therapy sessions
We investigate the automatic processing of child speech therapy sessions using ultrasound visual biofeedback, with a specific focus on complementing acoustic features with ultrasound images of the tongue for the tasks of speaker diarization and time-alignment of target words.
LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization
cvqluu/nn-similarity-diarization
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More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction.
End-to-End Neural Speaker Diarization with Permutation-Free Objectives
To realize such a model, we formulate the speaker diarization problem as a multi-label classification problem, and introduces a permutation-free objective function to directly minimize diarization errors without being suffered from the speaker-label permutation problem.
Robust speaker recognition using unsupervised adversarial invariance
rperi/speaker-embeddings-UAI-inference
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In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations.
Supervised online diarization with sample mean loss for multi-domain data
Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network.
