Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a novel probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meeting room, equipped with a small microphone array and multiple uncalibrated cameras. Our framework is based on a mixed-state dynamic graphical model defined on a multiperson state-space, which includes the explicit definition of a proximity-based interaction model. The model integrates audiovisual (AV) data through a novel observation model. Audio observations are derived from a source localization algorithm. Visual observations are based on models of the shape and spatial structure of human heads. Approximate inference in our model, needed given its complexity, is performed with a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. We present results-based on an objective evaluation procedure-that show that our framework 1) is capable of locating and tracking the position and speaking activity of multiple meeting participants engaged in real conversations with good accuracy, 2) can deal with cases of visual clutter and occlusion, and 3) significantly outperforms a traditional sampling-based approach

Distant microphones permit to process spontaneous multiparty speech with very little constraints on speakers, as opposed to close-talking microphones. Minimizing the constraints on speakers permits a large diversity of applications, including meeting summarization and browsing, surveillance, hearing aids, and more natural human-machine interaction. Such applications of distant microphones require to determine where and when the speakers are talking. This is inherently a multisource problem, because of background noise sources, as well as the natural tendency of multiple speakers to talk over each other. Moreover, spontaneous speech utterances are highly discontinuous, which makes it difficult to track the multiple speakers with classical filtering approaches, such as Kalman filtering of particle filters. As an alternative, this paper proposes a probabilistic framework to determine the trajectories of multiple moving speakers in the short-term only, i.e., only while they speak. Instantaneous location estimates that are close in space and time are grouped into ldquoshort-term clustersrdquo in a principled manner. Each short-term cluster determines the precise start and end times of an utterance and a short-term spatial trajectory. Contrastive experiments clearly show the benefit of using short-term clustering, on real indoor recordings with seated speakers in meetings, as well as multiple moving speakers.

Detection is usually done by comparing some criterion to a threshold. It is often desirable to keep a performance metric such as false alarm rate constant across conditions. Using training data to select the threshold may lead to suboptimal results on test data recorded in different conditions. This paper investigates unsupervised approaches, where no training data is used. A probabilistic model is fitted on the test data using the EM algorithm, and the threshold value is selected based on the model. The proposed approach (1) does not use training data, (2) uses the test data itself to compensate for simplifications inherent to the model, and (3) permits the use of more complex models in a straightforward manner. On a microphone array speech detection task, the proposed unsupervised approach achieves similar or better results than the "training" approach. The methodology is general and may be applied to other contexts than microphone arrays, and other performance metrics than FAR

Detection and localization of speakers with microphone arrays is a difficult task due to the wideband nature of speech signals, the large amount of overlap between speakers in spontaneous conversations, and the presence of noise sources. Many existing audio multi-source localization methods rely on prior knowledge of the sectors containing active sources and/or the number of active sources. The paper proposes sector-based, frequency-domain approaches that address both detection and localization problems by measuring relative phases between microphones. The first approach is similar to delay-sum beamforming. The second approach is novel: it relies on systematic optimization of a centroid in phase space, for each sector. It provides a major, systematic improvement over the first approach as well as over previous work. Very good results are obtained on more than one hour of recordings in real meeting room conditions, including cases with up to 3 concurrent speakers.

Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meeting room, equipped with a small microphone array and multiple uncalibrated cameras. Our framework is based on a mixed-state dynamic graphical model defined on a multiperson state-space, which includes the explicit definition of a proximity-based interaction model. The model integrates audio-visual (AV) data through a novel observation model. Audio observations are derived from a source localization algorithm. Visual observations are based on models of the shape and spatial structure of human heads. Approximate inference in our model, needed given its complexity, is performed with a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. We present results -based on an objective evaluation procedure-that show that our framework (1) is capable of locating and tracking the position and speaking activity of multiple meeting participants engaged in real conversations with good accuracy; (2) can deal with cases of visual clutter and partial occlusion; and (3) significantly outperforms a traditional sampling-based approach.

Widrow's interference canceller adapted by the normalized LMS (NLMS) is a standard approach for separating signals from multiple speakers, for example from the driver (target) and the codriver (interference) in a car. In practice, the adaptation must be carried out only when the interferer is dominant, i.e. only when some estimate of the signal-to-interference ratio (SIR) is below a certain threshold. In this paper, we present the implicitely controlled LMS (ILMS), a modification of the NLMS. ILMS adaptation is performed continuously using a variable step-size, whose design implicitly detects dominance of the interferer over target activity. Specific measures are taken to guarantee the stability during adaptation. Theoretical analysis of the ILMS transient convergence and stability conditions prove significant improvement with respect to the original NLMS. Experimental results on real in-car data assess the predicted behavior.

This paper proposes a simple, computationally efficient 2-mixture model approach to discrimination between speech and background noise. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algorithm. A first application to sector-based, joint audio source localization and detection, using multiple microphones, confirms that the model can provide major enhancement. A second application to the single channel speech recognition task in a noisy environment yields major improvement on stationary noise and promising results on non-stationary noise. 1.

This paper investigates the recognition of group actions in meetings. A framework is employed in which group actions result from the interactions of the individual participants. The group actions are modeled using different HMM-based approaches, where the observations are provided by a set of audiovisual features monitoring the actions of individuals. Experiments demonstrate the importance of taking interactions into account in modeling the group actions. It is also shown that the visual modality contains useful information, even for predominantly audio-based events, motivating a multimodal approach to meeting analysis.

The paper presents a new approach toward automatic annotation of meetings in terms of speaker identities and their locations. This is achieved by segmenting the audio recordings using two independent sources of information: magnitude spectrum analysis and sound source localization. We combine the two in an appropriate HMM framework. There are three main advantages of this approach. First, it is completely unsupervised, i.e. speaker identities and number of speakers and locations are automatically inferred. Second, it is threshold-free, i.e. the decisions are made without the need of a threshold value which generally requires an additional development dataset. The third advantage is that the joint segmentation improves over the speaker segmentation derived using only acoustic features. Experiments on a series of meetings recorded in the IDIAP smart meeting room demonstrate the effectiveness of this approach.

The paper proposes a technique that segments audio according to speakers and based on their location. In many multi-party conversations, such as meetings, the location of participants is restricted to a small number of regions, such as seats around a table, or at a whiteboard. In such cases, segmentation according to these discrete regions would be a reliable means of determining speaker turns. We propose a system that uses microphone pair time delays as features to represent speaker locations. These features are integrated in a GMM/HMM framework to determine an optimal segmentation of the audio according to location. The HMM framework also allows extensions to recognise more complex structures, such as the presence of two simultaneous speakers. Experiments testing the system on real recordings from a meeting room show that the proposed location features can provide greater discrimination than standard cepstral features, and also demonstrate the success of an extension to handle dual-speaker overlap.

The paper investigates the recognition of group actions in meetings by modeling the joint behaviour of participants. Many meeting actions, such as presentations, discussions and consensus, are characterised by similar or complementary behaviour across participants. Recognising these meaningful actions is an important step towards the goal of providing effective browsing and summarisation of processed meetings. A corpus of meetings was collected in a room equipped with a number of microphones and cameras. The corpus was labeled in terms of a predefined set of meeting actions characterised by global behaviour. In experiments, audio and visual features for each participant are extracted from the raw data and the interaction of participants is modeled using HMM-based approaches. Initial results on the corpus demonstrate the ability of the system to recognise the set of meeting actions.

We present a probabilistic method for audio-visual (AV) speaker tracking, using an uncalibrated wide-angle camera and a micro- phone array. The algorithm fuses 2-D object shape and audio information via importance particle filters (I-PFs), allowing for the asymmetrical integration of AV information in a way that efficiently exploits the complementary features of each modality. Audio localization information is used to generate an importance sampling (IS) function, which guides the random search process of a particle filter towards regions of the configuration space likely to contain the true configuration (a speaker). The measurement process integrates contour-based and audio observations, which results in reliable head tracking in realistic scenarios. We show that imperfect single modalities can be combined into an algorithm that automatically initializes and tracks a speaker, switches between multiple speakers, tolerates visual clutter, and recovers from total AV object occlusion, in the context of a multimodal meeting room.

This paper proposes a technique that segments audio according to speakers based on their location. In many multi-party conversations, such as meetings, the location of participants is restricted to a small number of regions, such as seats around a table, or at a whiteboard. In such cases, segmentation according to these discrete regions would be a reliable means of determining speaker turns. We propose a system that uses microphone pair time delays as features to represent speaker locations. These features are integrated in a GMM/HMM framework to determine an optimal segmentation of the audio according to location. The HMM framework also allows extensions to recognize more complex structure, such as the presence of two simultaneous speakers. Experiments testing the system on real recordings from a meeting room show that the proposed location features can provide greater discrimination than standard cepstral features, and also demonstrate the success of an extension to handle dual-speaker overlap.

Speaker turn detection is an important task for many speech processing applications. However, accurate segmentation can be hard to achieve if there are multiple concurrent speakers (overlap), as is typically the case in multi-party conversations. In such cases, the location of the speaker, as measured using a microphone array, may provide greater discrimination than traditional spectral features. This was verified in previous work which obtained a global segmentation in terms of single speaker classes, as well as possible overlap combinations. However, such a global strategy suffers from an explosion of the number of overlap classes, as each possible combination of concurrent speakers must be modeled explicitly. In this paper, we propose two alternative schemes that produce an individual segmentation decision for each speaker, implicitly handling all overlapping speaker combinations. The proposed approaches also allow straightforward online implementations. Experiments are presented comparing the segmentation with that obtained using the previous system. 1.