Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

Computationally Efficient and Robust BIC-Based Speaker Segmentation

An algorithm for automatic speaker segmentation based on the Bayesian information criterion (BIC) is presented. BIC tests are not performed for every window shift, as previously, but when a speaker change is most probable to occur. This is done by estimating the next probable change point thanks to a model of utterance durations. It is found that the inverse Gaussian fits best the distribution of utterance durations. As a result, less BIC tests are needed, making the proposed system less computationally demanding in time and memory, and considerably more efficient with respect to missed speaker change points. A feature selection algorithm based on branch and bound search strategy is applied in order to identify the most efficient features for speaker segmentation. Furthermore, a new theoretical formulation of BIC is derived by applying centering and simultaneous diagonalization. This formulation is considerably more computationally efficient than the standard BIC, when the covariance matrices are estimated by other estimators than the usual maximum-likelihood ones. Two commonly used pairs of figures of merit are employed and their relationship is established. Computational efficiency is achieved through the speaker utterance modeling, whereas robustness is achieved by feature selection and application of BIC tests at appropriately selected time instants. Experimental results indicate that the proposed modifications yield a superior performance compared to existing approaches

Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. T. Theodorou, I. Mpoas, A. Lazaridis, N. Fakotakis, 'Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News', International Journal on Artificial Intelligence Tools, Vol. 26 (2), April 2017, 1750005 (13 pages), DOI: 10.1142/S021821301750005. © The Author(s).In this paper we describe an automatic sound recognition scheme for radio broadcast news based on principal component clustering with respect to the discrimination ability of the principal components. Specifically, streams of broadcast news transmissions, labeled based on the audio event, are decomposed using a large set of audio descriptors and project into the principal component space. A data-driven algorithm clusters the relevance of the components. The component subspaces are used by sound type classifier. This methodology showed that the k-nearest neighbor and the artificial intelligent network provide good results. Also, this methodology showed that discarding unnecessary dimension works in favor on the outcome, as it hardly deteriorates the effectiveness of the algorithms.Peer reviewe

An Information Theoretic Approach to Speaker Diarization of Meeting Recordings

In this thesis we investigate a non parametric approach to speaker diarization for meeting recordings based on an information theoretic framework. The problem is formulated using the Information Bottleneck (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, the IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. The distance between speech segments is selected as the Jensen-Shannon divergence as it arises from the IB objective function optimization. In the first part of the thesis, we explore IB based diarization with Mel frequency cepstral coefficients (MFCC) as input features. We study issues related to IB based speaker diarization such as optimizing the IB objective function, criteria for inferring the number of speakers. Furthermore, we benchmark the proposed system against a state-of-the-art systemon the NIST RT06 (Rich Transcription) meeting data for speaker diarization. The IB based system achieves similar speaker error rates (16.8%) as compared to a baseline HMM/GMM system (17.0%). This approach being non parametric clustering, perform diarization six times faster than realtime while the baseline is slower than realtime. The second part of thesis proposes a novel feature combination system in the context of IB diarization. Both speaker clustering and speaker realignment steps are discussed. In contrary to current systems, the proposed method avoids the feature combination by averaging log-likelihood scores. Two different sets of features were considered – (a) combination of MFCC features with time delay of arrival features (b) a four feature stream combination that combines MFCC, TDOA, modulation spectrum and frequency domain linear prediction. Experiments show that the proposed system achieve 5% absolute improvement over the baseline in case of two feature combination, and 7% in case of four feature combination. The increase in algorithm complexity of the IB system is minimal with more features. The system with four feature input performs in real time that is ten times faster than the GMM based system

Audio segmentation-by-classification approach based on factor analysis in broadcast news domain

This paper studies a novel audio segmentation-by-classification approach based on factor analysis. The proposed technique compensates the within-class variability by using class-dependent factor loading matrices and obtains the scores by computing the log-likelihood ratio for the class model to a non-class model over fixed-length windows. Afterwards, these scores are smoothed to yield longer contiguous segments of the same class by means of different back-end systems. Unlike previous solutions, our proposal does not make use of specific acoustic features and does not need a hierarchical structure. The proposed method is applied to segment and classify audios coming from TV shows into five different acoustic classes: speech, music, speech with music, speech with noise, and others. The technique is compared to a hierarchical system with specific acoustic features achieving a significant error reduction

An Information Theoretic Approach to Speaker Diarization of Meeting Data

A speaker diarization system based on an information theoretic framework is described. The problem is formulated according to the {\em Information Bottleneck} (IB) principle. Unlike other approaches where the distance between speaker segments is arbitrarily introduced, IB method seeks the partition that maximizes the mutual information between observations and variables relevant for the problem while minimizing the distortion between observations. This solves the problem of choosing the distance between speech segments, which becomes the Jensen-Shannon divergence as it arises from the IB objective function optimization. We discuss issues related to speaker diarization using this information theoretic framework such as the criteria for inferring the number of speakers, the trade-off between quality and compression achieved by the diarization system, and the algorithms for optimizing the objective function. Furthermore we benchmark the proposed system against a state-of-the-art system on the NIST RT06 (Rich Transcription) data set for speaker diarization of meeting. The IB based system achieves a Diarization Error Rate of $23.2\%$ as compared to $23.6\%$ of the baseline system. This approach being mainly based on non-parametric clustering, it runs significantly faster then the baseline HMM/GMM based system, resulting in faster-then-real-time diarization

AGGLOMERATIVE INFORMATION BOTTLENECK FOR SPEAKER DIARIZATION OF MEETINGS DATA

In this paper, we investigate the use of agglomerative Information Bottleneck (aIB) clustering for the speaker diarization task of meetings data. In contrary to the state-of-the-art diarization systems that models individual speakers with Gaussian Mixture Models, the proposed algorithm is completely non parametric . Both clustering and model selection issues of non-parametric models are addressed in this work. The proposed algorithm is evaluated on meeting data on the RT06 evaluation data set. The system is able to achieve Diarization Error Rates comparable to state-of-the-art systems at a much lower computational complexity