iowa state university developmental robotics laboratory unsupervised segmentation of audio speech...

Iowa State UniversityDevelopmental Robotics Laboratory

Unsupervised Segmentation of Audio Speech using the Voting Experts Algorithm

Matthew Miller, Alexander StoytchevDevelopmental Robotics Lab

Department of Electrical and Computer Engineering Iowa State University

[email protected], [email protected]/~mamille/


Language: A Grand Challenge• A working example• Automatically acquires

language• Well studied


Statistical Learning Experiments

• Saffran et. al. (1996): 8-month-olds can segment speech.

Artificial Language:tupiro golabu bedaku padoti

Language: tu pi ro go la bu be da kuTransition Prob: 1.0 1.0 .25 1.0 1.0 .25 1.0 1.0 ...

Acclimate

Novel Word

• Hypothesis: Infants use local minima in single syllable transition probabilities to segment speech streams.


Voting Experts

• An algorithm for unsupervised segmentation• Key Idea: Natural “chunks” have:

– Low Internal Information– High Boundary Entropy

itwasabrightcolddayinaprilandtheclockswere

))"log(Pr(")"(" brightbrightI

)"(")"(" rightcIbrightI


Voting Experts

• An algorithm for unsupervised segmentation• Key Idea: Natural “chunks” have:

– Low Internal Information– High Boundary Entropy

itwasabrightcolddayinaprilandtheclockswere

)"(")"|"Pr()"("

brightIbrightbrightE

)"(")"(" brighEbrightE


VE Implementation (Cohen 2006)

1. Build an n-gram trie from text.2. Slide a window along the text sequence3. Two experts vote how to break the window

1. One minimizes internal info2. Other maximizes boundary entropy

i t w a s a b r i g h t c o l d d a y i n a p r i lWindow

1

windowts

II

..

)]()([min ,)"(")"(" abrigIasI





i t w a s a b r i g h t c o l d d a y i n a p r i lWindow

2

windowts

E

..

)]([max )"("asaE





4. Break at vote peaks

i t w a s a b r i g h t c o l d d a y i n a p r i l

i | t | w | a | s | a | b | r | i | g | h | t | c | o | l | d0

3

1

0

3

2

0

1

1

0

0

6

1

0

0


VE Results• Results are surprisingly good on text

– Especially giving its simplicity– Accuracy and Hit rate about 75%

• Seems to capture something about the nature of “chunks”

• Can we use this algorithm to segment real audio?

It was a br igh t


Acoustic Model


Acoustic Model

• Cluster spectral features using a GGSOM


Acoustic Model

• Cluster spectral features using a GGSOM• Collapse state sequence


Acoustic Model

• Cluster spectral features using a GGSOM• Collapse state sequence• Run VE to get breaks


Experiments and Results• Used the model to segment “1984”

– CD 1 of audio book (40 mins)– Chosen for length, consistency– Evaluation: Human graders


New Experiments• Trained on infant datasets

• Tested on manually generated keys

Stream A:tupiro golabu bedaku padoti

Stream B:dapiku tilado pagotu burobi

Train Train

Train Train

Test Test

Test Test

Acoustic Model A

Acoustic Model B

VE Model A

VE Model B

Key A

Key B


New Experiments• Trained on infant datasets

• Tested on manually generated keys

Stream A:tupiro golabu bedaku padoti

Stream B:dapiku tilado pagotu burobi

Test TestTes

t Test

Acoustic Model A

Acoustic Model B

VE Model A

VE Model B

Key B

Key A


Results• Experiment 1

– Accuracy: 50% on all induced breaks– Hit Rate: 75% of word breaks– Significantly better than chance

• Experiment 2– Accuracy: 16% on all induced breaks– Hit Rate: 1% of word breaks– Worse than chance– 18 breaks, 3 correct


Conclusions and Future Work• VE Model can be used to segment audio

• Can reproduce the results of Infant studies

• May model part of the human chunking mechanism

• Have built more sophisticated acoustic models– Better results (nearly perfect)


Thank You• www.cs.iastate.edu/~mamille/

iowa state university developmental robotics laboratory unsupervised segmentation of audio speech...

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