modeling individual and modeling individual and group actions in meetings group actions in meetings

with layered HMMswith layered HMMs

dong zhang, daniel gatica-perez

samy bengio, iain mccowan, guillaume lathoud

idiap research institute

martigny, switzerland

meetings as sequences of actions

– human interaction

• similar/complementary roles

• individuals constrained by group

– agenda: prior sequence • discussion points• presentations• decisions to be made

– minutes: posterior sequence • key phases• summarized discussions• decisions made

the goal: recognizing sequences of meeting actions

Presentation Group Discussion

Whether Budget

High HighNeutral

Discussion Phase

Topic

Group Interest Level

Information Sharing Decision MakingGroup Task

Timeline

meeting views

group-level actions = meeting actions

our work: two-layer HMMs

• decompose the recognition problem• both layers use HMMs

– individual action layer: I-HMM: various models – group action layer: G-HMM

our work in detail

1. definition of meeting actions

2. audio-visual observations

3. action recognition

4. results

D. Zhang et al, “Modeling Individual and Group Actions in Meetings with Layered HMMs”, IEEE CVPR Workshop on Event Mining, 2004.

N. Oliver et al, ICMI 2002.

I. McCowan et al, ICASSP 2003, PAMI 2005.

1. defining meeting actions

• multiple parallel views

– tech-based: what we can recognize?

– application-based: respond to user needs

– psychology-based: coding schemes from social psychology

• each view a set of actions

A = { A1, A2, A3, A4, …, AN }

• actions in a set– consistent: one view, answering one question– mutually exclusive– exhaustive

multi-modal turn-taking

• describes the group discussion state

A = { ‘discussion’,‘monologue’ (x4), ‘white-board’,‘presentation’,‘note-taking’,‘monologue + note-taking’ (x4),‘white-board + note-taking’,

‘presentation + note-taking’}

• individual actionsI = { ‘speaking’,

‘writing’, ‘idle’}

• actions are multi-modal in nature

example

Presentation Used

Person 2 W S W

Person 1 S S W

Person 3 W S S W

Person 4 S W S

Whiteboard Used

Monologue1+ Note-taking

Group Action DiscussionPresentation+ Note-taking

Whiteboard+ Note-taking

W

W

2. audio-visual observations

audio• 12 channels, 48 kHz • 4 lapel microphones• 1 microphone array

video• 3 CCTV cameras

all synchronized

multimodal feature extraction: audio

• microphone array– speech activity (SRP-PHAT)

• seats • presentation/whiteboard area

– speech/silence segmentation

• lapel microphones – speech pitch – speech energy– speaking rate

multimodal feature extraction: video

• head + hands blobs– skin colour models (GMM)– head position– hands position + features (eccentricity,size,orientation) – head + hands blob motion

• moving blobs from background subtraction

3. recognition with two-layer HMM

• each layer trained independently

• trained as in ASR (Torch)

• simultaneous segmentation and recognition

• compared with single-layer HMM

– smaller observation spaces

– I-HMM trained with much more data

– G-HMM less sensitive to feature variations

– combinations can be explored

models for I-HMM

• early integration

– all observations concatenated– correlation between streams– frame-synchronous streams

• asynchronous (Bengio, NIPS 2002)– a and v streams with single state sequence– states emit on one or both streams, given a sync variable– inter-stream asynchrony

• multi-stream (Dupont, TMM 2000)

– HMM per stream (a or v), trained independently– decoding: weighted likelihoods combined at each frame– little inter-stream asynchrony– multi-band and a-v ASR

linking the two layers

• hard decision

i-action model with highest probability outputs 1; all other models output 0.

• soft decision

outputs probability for each individual action model

Audio-visual features

HD: (1, 0, 0)SD: (0.9, 0.05, 0.05)

• 59 meetings (30/29 train/test)

• four-people, five-minute

• scripts

– schedule of actions

– natural behavior

• features: 5 f/s

4. experiments: data + setup

mmm.idiap.ch

performance measures

• individual actions: frame error rate (FER)

• group actions: action error rate (AER)

•Subs: number of substituted actions

•Del: number of deleted actions

•Ins: number of added actions

•Total actions: number of target actions

results: individual actions

(0.8,0.2)

43000 frames

(0.2-2.2s)

• visual-only audio-only audio-visual• asynchronous effects between modalities• accuracy: speaking: 96.6 %, writing: 90.8%, idle: 81.5%

results: group actions

• multi-modality outperforms single modalities

• two-layer HMM outperforms single-layer HMM for a-only, v-only and a-v

• best model: A-HMM

• soft decision slightly better than hard decision

8% improvement, significant at 96% level

action-based meeting structuring

conclusions

• structuring meetings as sequences of meeting actions

– layered HMMs successful for recognition

– turn-taking patterns: useful for browsing

– public dataset, standard evaluation procedures

• open issues

– less training data (unsupervised, acm mm04)

– other relevant actions (interest-level, icassp05)

– other features (words, emotions)

– efficient models for many interacting streams

Linking Two Layers (1)

Linking Two Layers (2)

Normalization

Please refer to:D. Zhang, et al “Modeling Individual and Group Actions in Meetings: a Two-Layer HMM Framework”. In IEEE Workshop on Event Mining, CVPR, 2004.