An overview of automatic speaker diarization systems

By S. Tranter, Douglas A. Reynolds. IEEE Transactions on Audio, Speech, and Language Processing, 2006

666 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e9e43ddcc04b4c796d6b4ae0b532d2d2b2fdf3cb

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Citations in the corpus

Social signal processing: Survey of an emerging domain (2009, background): “In the case of audio recordings, person detection is called speaker segmentation or speaker diarization and consists of splitting the speech recordings into intervals corresponding to a single voice, recognizing automatically who talks when (see [189] for an extensive survey).”
Neural Speaker Diarization with Speaker-Wise Chain Rule (2020, background): “Speaker diarization is the process of partitioning audio according to the speaker identity, which is an essential step for multispeaker audio applications such as generating written minutes of meetings [1, 2].”
A Sticky HDP-HMM With Application to Speaker Diarization (2009, neutral): “BOX 90251 DURHAM , NC 27701 E-MAIL : fox@stat.duke.edu DEPARTMENT OFCOMPUTERSCIENCE 115 WATERMAN STREET, BOX 1910 PROVIDENCE, RI 02912 E-MAIL : sudderth@cs.brown.edu”
Bayesian nonparametric hidden semi-Markov models (2012, background): “For example, in a speaker diarization (or who-spoke-when) problem, we are given a single audio recording of a meeting and wish to infer the number of speakers present, when they speak, and some characteristics governing their speech patterns (Tranter and Reynolds, 2006; Fox et al., 2008).”
A Review of Speaker Diarization: Recent Advances with Deep Learning (2021, background): “In [2], various speaker diarization systems and their subtasks in the”
Speaker Diarization: A Review of Recent Research (2010, neutral): “sented in [2], although it predominantly focuses its attention to”
End-to-End Neural Speaker Diarization with Permutation-Free Objectives (2019, background): “It is an important process for a wide variety of applications such as information retrieval from broadcast news, meetings, and telephone conversations [1, 2].”
Artificial neural network features for speaker diarization (2014, background): “It is an unsupervised task, as there is no a-priori knowledge of the speakers or the number of speakers in a conversation [1, 2].”
pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis (2015, background): “Speaker diarization is the task of automatically answering the question “who spoke when”, given a speech recording [8, 9].”
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone (2021, methodology): “To handle this, one approach is to first apply speaker diarization [11, 12, 13] to detect the utterance boundaries for each speaker and then perform ASR for each utterance.”
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals (2020, methodology): “, speaker diarization [40, 1, 32]), which lose an end-to-end transduction function, unlike our proposed method.”
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing (2012, neutral): “This is the second step of the process and it involves technologies like face detection [80], speaker segmentation [81], tracking [82], etc.”
Detection and Classification of Acoustic Scenes and Events (2015, methodology): “For example, the DARPA EARS Rich Transcription evaluations (2002–2009) focussed on speaker-diarization tasks, applied to broadcast news as well as recordings of meetings [18].”