A review on speaker diarization systems and approaches

Moattar, Mohammad Hossein; Homayounpour, Mohammad Mehdi

doi:10.1016/j.specom.2012.05.002

Cited by 85 publications

(43 citation statements)

References 138 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given any two i-vectors and , the cosine similarity score is given as (2) Equivalently, this means we can normalize the i-vectors by their respective magnitudes such that they all live on the unit hypersphere and the measure of the distance between two i-vectors is given by their angle.…”

Section: B the Total Variability Approachmentioning

confidence: 99%

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Shum

Dehak

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

161

View full text Add to dashboard Cite

Abstract-In speaker diarization, standard approaches typically perform speaker clustering on some initial segmentation before refining the segment boundaries in a re-segmentation step to obtain a final diarization hypothesis. In this paper, we integrate an improved clustering method with an existing re-segmentation algorithm and, in iterative fashion, optimize both speaker cluster assignments and segmentation boundaries jointly. For clustering, we extend our previous research using factor analysis for speaker modeling. In continuing to take advantage of the effectiveness of factor analysis as a front-end for extracting speaker-specific features (i.e., i-vectors), we develop a probabilistic approach to speaker clustering by applying a Bayesian Gaussian Mixture Model (GMM) to principal component analysis (PCA)-processed i-vectors. We then utilize information at different temporal resolutions to arrive at an iterative optimization scheme that, in alternating between clustering and re-segmentation steps, demonstrates the ability to improve both speaker cluster assignments and segmentation boundaries in an unsupervised manner. Our proposed methods attain results that are comparable to those of a state-of-the-art benchmark set on the multi-speaker CallHome telephone corpus. We further compare our system with a Bayesian nonparametric approach to diarization and attempt to reconcile their differences in both methodology and performance.

show abstract

Section: B the Total Variability Approachmentioning

confidence: 99%

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Shum

Dehak

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

161

View full text Add to dashboard Cite

show abstract

“…Speaker diarization is the "who spoke when" problem, also called speaker indexing (Wellekens, 2001;Miró et al, 2012;Moattar and Homayounpour, 2012). The input is audio features sampled at 100 Hz frame rate, and the output is framelabels indicating speaker identify for each frame.…”

Section: Speaker Diarizationmentioning

confidence: 99%

An automated medical scribe for documenting clinical encounters

Finley¹,

Edwards²,

Robinson³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: De

View full text Add to dashboard Cite

A medical scribe is a clinical professional who charts patient-physician encounters in real time, relieving physicians of most of their administrative burden and substantially increasing productivity and job satisfaction. We present a complete implementation of an automated medical scribe. Our system can serve either as a scalable, standardized, and economical alternative to human scribes; or as an assistive tool for them, providing a first draft of a report along with a convenient means to modify it. This solution is, to our knowledge, the first automated scribe ever presented and relies upon multiple speech and language technologies, including speaker diarization, medical speech recognition, knowledge extraction, and natural language generation.

show abstract

“…Speaker diarization research mainly tackles the simultaneous estimation of speaker segmentation (voice activity detection) and clustering (number of speaker estimation). Beside monaural signal based methods [1], [2], microphone array technologies tackles this by introducing spatial information about the speakers. However, most of the existing methods assume that the microphone location is given to estimate the direction of arrival of speakers [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Nakamura

Mizumoto

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-This paper presents a method for estimating the number, as well as the activity periods of spatially distributed sound sources using an uncalibrated microphone array. This methodology is applied for the purposes of speaker diarization. In general, speaker diarization has difficulty with: 1) estimating the number of sound sources (speakers), and 2) activity detection of multiple sound sources including overlap of utterances. Several microphone array based techniques have already tackled these challenges. However, existing methods mainly assume that the steering vectors for the microphone array are calibrated in advance to identify sound sources, which is difficult to satisfy when ad-hoc or flexible microphone arrays are used. Thus our approach estimates the number of sound sources blindly in two steps. First, Time Delay of Arrival (TDOA) of the observed signal is clustered. Second, the sound source activity is detected by clustering the long-term spatial spectrum using the TDOA based steering vector for each cluster. The validity of the algorithm is confirmed by both synthesized signals and a real-world flexible microphone array application.

show abstract

A review on speaker diarization systems and approaches

Cited by 85 publications

References 138 publications

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach

An automated medical scribe for documenting clinical encounters

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Contact Info

Product

Resources

About