Ayako Ikeno and John H.L. Hansen

Email: {ikeno, John.Hansen}@utdallas.edu Slide * IAFPA-2006 Center for Robust Speech Systems SLIDES by John H.L. Hansen, 2006

First observed by Etienne Lombard in 1911

Change in speech production in response to noise to increase communication performance

Lombard Test - standard test for hearing loss in U.S. (ASHA) measure dB-SPL change in speech production

Hansen (1988) evaluation of 200 features with +10,000 statistical tests on 11 different stressed speech conditions to quantify changes in speech production


IAFPA-06: focus on Lombard Effect Audio samples for the perceptual experiment were extracted from UTScope corpus.

Speech under COgnitive and Physical stress & Emotion

Consists of 4 Domains Lombard Effect noise levels & types Physical Stress stair climbing/stepper Cognitive Stress driving (simulator & actual) Emotion (Angry, Fear, Anxiety, Frustration)


Goal: obtain Lombard Speech at different noise levelsQuantify ground truth with biometric analysis Lombard Effect Speech 9 conditions (3 noise, 3 levels)1 sec. durationPink Noise65,75,85 dB-SPLHighway Noise(windows open)70,80,90 dB-SPLLarge Crowd Noise70,80,90 dB-SPL


UTScopePINK NOISE 65, 75, 86 dB-SPLHIGHWAY DRIVING,WINDOWS HALF OPEN70, 80 ,90 dB-SPLLARGE CROWD NOISE70, 80, 90 dB-SPLPURETONE HEARING SCREENINGOPEN-AIR HEADPHONESFOR SPEECH FEEDBACKNOISE LEVELS CALIBRATEDWITH QUEST SLM


UTScope20 TIMIT SENTENCES5 DIGIT STRINGS1 MINUTE SPONTANEOUS SPEECH100 SPEAKERS8-CHANNEL DAT RECORDERP-MICCLOSE-TALKING MICFAR-FIELD MIC


The ASHA-certified sound booth and recording equipments


Male Lombard Male Neutral Lombard Effect impacts Temporal and Spectral Structure (as expected) Evaluation: Perceptual Experiments to assess Speaker Recognition


Listener Test Speakers Corpus: UTScope Native US English speakers Female speakers only Speech ConditionsNoise TypeHighway drivingNoise Level90dB-SPL

ReferenceTestNL-LDNeutralLombardLD-LDLombardLombardNL-NLNeutralNeutral


Speech MaterialsRead speechTIMIT sentences: phonetically balanced3 sentences per audio sample (.wav, 16k Hz)Ref: Basketball can be an entertaining sport.My problem is, the cats meow always hurts my ears.The causeway ended abruptly at the shore.Test: Youngsters commonly love chocolate and candies as treats. December and January are nice months to spend in Miami. There were other farmhouses nearby.


Listener Test Listeners (12: 2f/10m May 06, -- 41 as of July 06)

India(4), China(1), Korea(1), Mexico(1), Pakistan(1), Thai(1), Turkey(1) US(1), Vietnam(1) Task: In-set vs. Out-of-set Speaker IdentificationReference/Training12 In-set Female speakersTest8 In-Set speakers 4 Out-of-Set speakers


Reference audio: Neutral Lombard Test audio: Neutral Lombard


The effect of speech condition: significant (p=.0024).Mismatched condition (NL-LD) accuracy: chance level (52%).Lombard speech (LD-LD, 79%): higher accuracy than neutral speech (NL-NL, 67%). Lombard effect may emphasize the speech characteristics, and improve accuracy on perceptual speaker ID.

Part-1

S7587.550

WK585075

RH332550

VP5025100

A676375

LG5025100

PA4213100

MH676375

NM423850

AD330100

MA4213100

MM676375

523979

Part-2

S677550

WK9210075

RH587525

VP758850

A758850

LG9288100

PA9210075

MH587525

NM50750

AD8375100

MA100100100

MM100100100

798763

Part-3

S50750

WK8310050

RH503875

VP757575

A838875

LG6750100

PA678825

MH75880

NM677550

AD9288100

MA585075

MM425025

677254

Results_all

527967

398772

796354

NL-LD

LD-LD

NL-NL

Speaker Category

Identification Accuracy %

In-Set vs. Out-of-Set Speaker IdentificationLombard Effect

Average

52

79

67

Average

Reference Speech and Test Speech Condictions

Accuracy %

In-Set vs. Out-of-Set Speaker IdentificationAccuracy AverageLombard Effecct

In-Out

398772

796354

NL-LD

LD-LD

NL-NL

Speaker Category

Accuracy %

In-Set vs. Out-of-Set Speaker Identification Lombard Effect

StatsView

75675088757550500

58928350100100757550

335850257538502575

5075752588751005075

677583638888755075

509267258850100100100

42926713100881007525

67587563758875250

42506738757550050

33839207588100100100

4210058131005010010075

671004263100507510025

All

Average

AverageIn-SetOut-of-Set

NL-LD523979

LD-LD798763

NL-NL677254

Average

NL-LD52

LD-LD79

NL-NL67

In-SetOut-of-Set

NL-LD3979

LD-LD8763

NL-NL7254


Automated System Performance (SUSAS Corpus)(See Hansen, et.al, The Impact of Speech Under `Stress' on Military Speech Technology, NATO Research & Tech. Org. RTO-TR-10, March 2000). Angry62%Lombard48%Loud74%5-74%LOSSThe trend hold the same for the automated system.


In-Set accuracy: affected by the speech condition significantly (p

Documents

Ayako Ikeno and John H.L. Hansen