Automatic speaker segmentation using multiple features and distance measures: a comparison of three approaches

This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian Information Criterion (BIC) are tested. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic thresholding followed by a fusion scheme, and finally applies BIC. The second method is a real-time one that uses a metric-based approach employing the line spectral pairs and the BIC to validate a potential speaker change point. The third method consists of three modules. In the first module, a measure based on second-order statistics is used; in the second module, the Euclidean distance and T2 Hotelling statistic are applied; and in the third module, the BIC is utilized. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. A comparison between the performance of the three systems is made based on t-statistics

Speaker change detection using BIC: a comparison on two datasets

Abstract — This paper addresses the problem of unsupervised speaker change detection. We assume that there is no prior knowledge on the number of speakers or their identities. Two methods are tested. The first method uses the Bayesian Information Criterion (BIC), investigates the AudioSpectrumCentroid and AudioWaveformEnvelope features, and implements a dynamic thresholding followed by a fusion scheme. The second method is a real-time one that uses a metric-based approach employing line spectral pairs (LSP) and the BIC criterion to validate a potential change point. The experiments are carried out on two different datasets. The first set was created by concatenating speakers from the TIMIT database and is referred to as the TIMIT data set. The second set was created by using recordings from the MPEG-7 test set CD1 and broadcast news and is referred to as the INESC dataset. I

A Speaker Diarization System for Studying Peer-Led Team Learning Groups

Peer-led team learning (PLTL) is a model for teaching STEM courses where small student groups meet periodically to collaboratively discuss coursework. Automatic analysis of PLTL sessions would help education researchers to get insight into how learning outcomes are impacted by individual participation, group behavior, team dynamics, etc.. Towards this, speech and language technology can help, and speaker diarization technology will lay the foundation for analysis. In this study, a new corpus is established called CRSS-PLTL, that contains speech data from 5 PLTL teams over a semester (10 sessions per team with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a LENA device (portable audio recorder) that provides multiple audio recordings of the event. Our proposed solution is unsupervised and contains a new online speaker change detection algorithm, termed G 3 algorithm in conjunction with Hausdorff-distance based clustering to provide improved detection accuracy. Additionally, we also exploit cross channel information to refine our diarization hypothesis. The proposed system provides good improvements in diarization error rate (DER) over the baseline LIUM system. We also present higher level analysis such as the number of conversational turns taken in a session, and speaking-time duration (participation) for each speaker.Comment: 5 Pages, 2 Figures, 2 Tables, Proceedings of INTERSPEECH 2016, San Francisco, US

Automatic speaker change detection with the Bayesian information criterion using MPEG-7 features and a fusion scheme

This paper addresses unsupervised speaker change detection, a necessary step for several indexing tasks. We assume that there is no prior knowledge either on the number of speakers or their identities. Features included in the MPEG-7 Audio Prototype are investigated such as the AudioWaveformEnvelope and the AudioSpecrtumCentroid. The model selection criterion is the Bayesian Information Criterion (BIC). A multiple pass algorithm is proposed. It uses a dynamic thresholding for scalar features and a fusion scheme so as to refine the segmentation results. It also models every speaker by a multivariate Gaussian probability density function and whenever new information is available, the respective model is updated. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. It is and demonstrated that the performance of the proposed multiple pass algorithm is better than that of other approaches

Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection

Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g., OpenSubtitle), and enhancing audio SCD with textual information. We formulate text-based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence-level attention. Experimental results show that neural networks consistently achieve better performance than feature-based approaches, and that our attention-based model significantly outperforms non-attention neural networks.Comment: In Proceedings of the ACM on Conference on Information and Knowledge Management (CIKM), 201

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

Systematic comparison of BIC-based speaker segmentation systems

Unsupervised speaker change detection is addressed in this paper. Three speaker segmentation systems are examined. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic fusion scheme, and applies the Bayesian Information Criterion (BIC). The second system consists of three modules. In the first module, a second-order statistic-measure is extracted; the Euclidean distance and the T2 Hotelling statistic are applied sequentially in the second module; and BIC is utilized in the third module. The third system, first uses a metric-based approach, in order to detect potential speaker change points, and then the BIC criterion is applied to validate the previously detected change points. Experiments are carried out on a dataset, which is created by concatenating speakers from the TIMIT database. A systematic performance comparison among the three systems is carried out by means of one-way ANOVA method and post hoc Tukey’s method

Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Most ongoing efforts have focused on training decoders on specific, stereotyped tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in natural settings requires adaptive strategies and scalable algorithms that require minimal supervision. Here we propose an unsupervised approach to decoding neural states from human brain recordings acquired in a naturalistic context. We demonstrate our approach on continuous long-term electrocorticographic (ECoG) data recorded over many days from the brain surface of subjects in a hospital room, with simultaneous audio and video recordings. We first discovered clusters in high-dimensional ECoG recordings and then annotated coherent clusters using speech and movement labels extracted automatically from audio and video recordings. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Our results show that our unsupervised approach can discover distinct behaviors from ECoG data, including moving, speaking and resting. We verify the accuracy of our approach by comparing to manual annotations. Projecting the discovered cluster centers back onto the brain, this technique opens the door to automated functional brain mapping in natural settings

Generative Modelling for Unsupervised Score Calibration

Score calibration enables automatic speaker recognizers to make cost-effective accept / reject decisions. Traditional calibration requires supervised data, which is an expensive resource. We propose a 2-component GMM for unsupervised calibration and demonstrate good performance relative to a supervised baseline on NIST SRE'10 and SRE'12. A Bayesian analysis demonstrates that the uncertainty associated with the unsupervised calibration parameter estimates is surprisingly small.Comment: Accepted for ICASSP 201