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Transcript of Multilingual Access to Large Spoken Archives Douglas W. Oard University of Maryland, College Park,...
Multilingual Access to Large Spoken Archives
Douglas W. OardUniversity of Maryland, College Park, MD, USA
MALACH Project’s Goal
Dramatically improve access to large multilingual spoken word collections
… by capitalizing on the unique characteristics of the Survivors of the Shoah Visual History Foundation's collection of videotaped oral history
interviews.
Spoken Word Collections
• Broadcast programming– News, interview, talk radio, sports, entertainment
• Scripted stories– Books on tape, poetry reading, theater
• Spontaneous storytelling– Oral history, folklore
• Incidental recording– Speeches, oral arguments, meetings, phone calls
Some Statistics
• 2,000 U.S. radio stations webcasting
• 250,000 hours of oral history in British Library
• 35 million audio streams indexed by SingingFish– Over 1 million searches per day
• ~100 billion hours of phone calls each year
Economics of the Web in 1995• Affordable storage
– 300,000 words/$
• Adequate backbone capacity– 25,000 simultaneous transfers
• Adequate “last mile” bandwidth– 1 second/screen
• Display capability– 10% of US population
• Effective search capabilities– Lycos, Yahoo
Spoken Word Collections Today• Affordable storage
– 300,000 words/$
• Adequate backbone capacity– 25,000 simultaneous transfers
• Adequate “last mile” bandwidth– 1 second/screen
• Display capability– 10% of US population
• Effective search capabilities– Lycos, Yahoo
1.5 million words/$
30 million
20% of capacity
38% recent use
Research Issues
• Acquisition
• Segmentation
• Description
• Synchronization
• Rights management
• Preservation
MALACH
Description Strategies• Transcription
– Manual transcription (with optional post-editing)
• Annotation– Manually assign descriptors to points in a recording– Recommender systems (ratings, link analysis, …)
• Associated materials– Interviewer’s notes, speech scripts, producer’s logs
• Automatic– Create access points with automatic speech processing
Key Results from TREC/TDT
• Recognition and retrieval can be decomposed– Word recognition/retrieval works well in English
• Retrieval is robust with recognition errors– Up to 40% word error rate is tolerable
• Retrieval is robust with segmentation errors– Vocabulary shift/pauses provide strong cues
Supporting Information Access
SourceSelection
Search
Query
Selection
Ranked List
Examination
Recording
Delivery
Recording
QueryFormulation
Search System
Query Reformulation and
Relevance Feedback
SourceReselection
Broadcast News Retrieval Study
• NPR OnlineManually prepared transcripts
Human cataloging
• SpeechBotAutomatic Speech Recognition
Automatic indexing
NPR Online
SpeechBot
Study Design• Seminar on visual and sound materials
– Recruited 5 students
• After training, we provided 2 topics– 3 searched NPR Online, 2 searched SpeechBot
• All then tried both systems with a 3rd topic– Each choosing their own topic
• Rich data collection– Observation, think aloud, semi-structured interview
• Model-guided inductive analysis– Coded to the model with QSR NVivo
Criterion-Attribute Framework
Relevance Criteria
Associated Attributes
NPR Online SpeechBot
Topicality
Story Type
Authority
Story title
Brief summary
Audio
Detailed summary
Speaker name
Audio
Detailed summary
Short summary
Story title
Program title
Speaker name
Speaker’s affiliation
Detailed summary
Brief summary
Audio
Highlighted terms
Audio
Program title
Some Useful Insights
• Recognition errors may not bother the system, but they do bother the user!
• Segment-level indexing can be useful
Shoah Foundation’s Collection• Enormous scale
– 116,000 hours; 52,000 interviews; 180 TB
• Grand challenges– 32 languages, accents, elderly, emotional, …
• Accessible– $100 million collection and digitization investment
• Annotated– 10,000 hours (~200,000 segments) fully described
• Users– A department working full time on dissemination
Example Video
Existing Annotations• 72 million untranscribed words
– From ~4,000 speakers
• Interview-level ground truth– Pre-interview questionnaire (names, locations, …)– Free-text summary
• Segment-level ground truth– Topic boundaries: average ~3 min/segment– Labels: Names, topic, locations, year(s)– Descriptions: summary + cataloguer’s scratchpad
Annotated Data Example
Subject PersonLocation-Time
Berlin-1939 Employment Josef Stein
Berlin-1939 Family life Gretchen Stein Anna Stein
Dresden-1939 Schooling Gunter Wendt Maria
Dresden-1939 Relocation Transportation-rail inte
rvie
w ti
me
MALACH Overview
AutomaticSearch
BoundaryDetection
InteractiveSelection
ContentTagging
SpeechRecognition
QueryFormulation
ASR SpontaneousAccentedLanguage switching
NLPComponents Multi-scale segmentation
Multilingual classificationEntity normalization Prototype
Evidence integrationTranslingual searchSpatial/temporal
UserNeeds
Observational studiesFormative evaluationSummative evaluation
MALACH Overview
AutomaticSearch
BoundaryDetection
InteractiveSelection
ContentTagging
SpeechRecognition
QueryFormulation
ASR SpontaneousAccentedLanguage switching
ASR Research Focus
• Accuracy– Spontaneous speech– Accented/multilingual/emotional/elderly– Application-specific loss functions
• Affordability– Minimal transcription– Replicable process
Application-Tuned ASR
• Acoustic model– Transcribe short segments from many speakers– Unsupervised adaptation
• Language model– Transcribed segments– Interpolation
ASR Game Plan
Hours Word
Language Transcribed Error Rate
English 200 39.6%
Czech 84 39.4%
Russian 20 (of 100) 66.6%
Polish
Slovak
As of May 2003
English Transcription Time
~2,000 hours to manually transcribe 200 hours from 800 speakers
Hours to transcribe 15 minutes of speech
Inst
ance
s (N
=83
0)
English ASR Error Rate
0
20
40
60
80
100
Wo
rd E
rro
r R
ate
Training: 65 hours (acoustic model)/200 hours (language model)
MALACH Overview
AutomaticSearch
BoundaryDetection
InteractiveSelection
ContentTagging
SpeechRecognition
QueryFormulation
UserNeeds
Observational studiesFormative evaluationSummative evaluation
Who Uses the Collection?
• History• Linguistics• Journalism• Material culture• Education• Psychology• Political science• Law enforcement
• Book• Documentary film• Research paper• CDROM• Study guide• Obituary• Evidence• Personal use
Discipline Products
Based on analysis of 280 access requests
Question Types
• Content– Person, organization– Place, type of place (e.g., camp, ghetto)– Time, time period– Event, subject
• Mode of expression– Language– Displayed artifacts (photographs, objects, …) – Affective reaction (e.g., vivid, moving, …)
• Age appropriateness
Observational Studies
• Four searchers– History/Political Science– Holocaust studies– Holocaust studies– Documentary filmmaker
• Sequential observation• Rich data collection
– Intermediary interaction– Semi-structured interviews– Observational notes– Think-aloud– Screen capture
• Four searchers– Ethnography
– German Studies
– Sociology
– High school teacher
• Simultaneous observation
• Opportunistic data collection– Intermediary interaction
– Semi-structured interviews
– Observational notes
– Focus group discussions
Workshop 1 (June) Workshop 2 (August)
Segment Viewer
Observed Selection Criteria
• Topicality (57%)Judged based on: Person, place, …
• Accessibility (23%)Judged based on: Time to load video
• Comprehensibility (14%)Judged based on: Language, speaking style
References to Named Entities
AttributesMentions
Selection Reformulation
Person
(N=138)
GenderCountry of birthNationalityDate of birthStatus, intervieweeStatus, parents
110101
221513111211
Place
(N=116)
CampCountryGhetto
10 8 7
451612
FunctionalityNeeded Function Boolean Search and Ranked Retrieval (13)
Testimony summary (12)
Pre-Interview Questionnaire search/viewer (9)
Rapid access (7)
Related/Alternative search terms (3)
Adding multiple search terms at once (2)
Keywords linked to segment number for easy access(1)
Multi-tasking (1)
Searching testimonies by places under ‘Experience Search’ (1)
Extensive editing within ‘My Project’ (1)
Desired Function Temporary saving of selected testimonies (4)
Remote access (3)
Integrated user tools for note taking (3)
Map presentation (2)
Reference tool (1)
More repositories (1)
Introductory video of system tutorial (1)
Help (1)
MALACH Overview
AutomaticSearch
BoundaryDetection
InteractiveSelection
ContentTagging
SpeechRecognition
QueryFormulation
NLPComponents Multi-scale segmentation
Multilingual classificationEntity normalization
scratchpad
transcript
“True” segmentation:transcripts aligned with scratchpad-based
boundaries
Hours Words Sentences Segments
Training 177.5 1,555,914 210,497 2,856
Test 7.5 58,913 7,427 168
Topic Segmentation
cataloguer
true
system output
missfalsealarm
Effect of ASR Errors
Rethinking the Problem
• Segment-then-label models planned speech well– Producers assemble stories to create programs– Stories typically have a dominant theme
• The structure of natural speech is different– Creation: digressions, asides, clarification, …– Use: intended use may affect desired granularity
• Documentary film: brief snippet to illustrate a point• Classroom teacher: longer self-contextualizing story
OntoLog: Labeling Unplanned Speech
• Manually assigned labels; start and end at any time– Ontology-based aggregation helps manage complexity
Goal
Use available data to estimate the temporal extent of labels in a way that optimizes the utility of the resulting estimates for interactive searching and browsing
Multi-Scale SegmentationL
abel
s
Time
Characteristics of the Problem
• Clear sequential dependencies– Living in Dresden negates living in Berlin
• Heuristic basis for class models– Persons, based on type of relationship– Date/Time, based on part-whole relationship– Topics, based on a defined hierarchy
• Heuristic basis for guessing without training– Text similarity between labels and spoken words
• Heuristic basis for smoothing– Sub-sentence retrieval granularity is unlikely
Manually Assigned Onset Marks
Subject PersonLocation-Time
Berlin-1939
Dresden-1939
Employment Josef SteinGretchen SteinAnna Stein
RelocationTransportation-rail
SchoolingGunter Wendt
Family Life
Maria
inte
rvie
w ti
me
Some Additional Results
• Named entity recognition– F > 0.8 (on manual transcripts)
• Cross-language ranked retrieval (on news)– Czech/English similar to other language pairs
Looking Forward: 2003
• Component development– ASR, segmentation, classification, retrieval
• Ranked retrieval test collection– 1,000 hours of English recognition– 25 judged topics in English and Czech
• Interactive retrieval– Integrating free text and thesaurus-based search
Relevance Categories
• Overall relevanceAssessment is informed by the assessments for the individual reasons for relevance (categories of relevance), but the relationship is not straightforward
• Provides direct evidence
• Provides indirect / circumstantial evidence
• Provides context(e.g., causes for the phenomenon of interest)
• Provides comparison (similarity or contrast, same phenomenon in different environment, similar phenomenon)
• Provides pointer to source of information
Scale for overall relevance
Strictly from the point of view of finding out about the topic, how useful is this segment for the requester? This judgment is made independently of whether another segment (or 25 other segments) give the same information.
4 Makes an important contribution to the topic, right on target
3 Makes an important contribution to the topic
2 Should be looked at for an exhaustive treatment of the topic
1 Should be looked at if the user wants to leave no stone unturned
0 No need to look at this at all
Direct relevanceDirect evidence for what the user asks for
Directly on topic, direct aboutness. The information describes the events or circumstances asked for or otherwise speaks directly to what the user is looking for. First-hand accounts are preferred, e.g., the testimony contains a report on the interviewee's own experience, or an eye-witness account on what happened, or self-report on how a survivor felt. Second-hand accounts (hearsay) are acceptable, such as a report on what an eyewitness told the interviewee or a report on how somebody else felt.
* Direct Evidence *- Evidence that stands on its own to prove an alleged fact, such as testimony of a witness who says she saw a defendant pointing a gun at a victim during a robbery. Direct proof of a fact, such as testimony by a witness about what that witness personally saw or heard or did. ('Lectric Law Library's Lexicon)
Indirect relevanceProvides indirect evidence on the topic, indirect aboutness (data from which one could infer, with some probability, something about the topic, what in law is known as circumstantial evidence) Such evidence often deals with events or circumstances that could not have happened or would not normally have happened unless the event or circumstance of interest (to be proven) has happened. It may also deal with events or circumstances that precede the events or circumstances of interest, either enabling them (establishing their possibility) or establishing their impossibility. This category takes precedence over context. One could say that provides indirect evidence also provides context (but the reverse is not true).
* Circumstances, Circumstantial Evidence * Circumstantial evidence is best explained by saying what it is not - it is not direct evidence from a witness who saw or heard something. Circumstantial evidence is a fact that can be used to infer another fact.
ContextProvides background / context for topic, sheds additional light on a topic, facilitates understanding that some piece of information is directly on topic.
So this category covers a variety of things. Things that influence, set the stage, or provide the environment for what the user asks for. (To take the law analogy again any things in the history of a person who has committed a crime that might explain why he committed it).
Includes support for or hindrance of an activity that is the topic of the query andactivities or circumstances that immediately follow on the activity or circumstance of interest.
In a way, this category is broader than indirect If a context element can serve as indirect evidence, indirect takes precedence.
Comparison
Provides information on similar / parallel situations or on a contrasting situation for comparison
The basic theme of what the user is interested in, but played out in a different place or time or type of situation.
Comparable segments will be those segments that provide information either on similar/parallel topics, or on contrasting topics. This type of relevance relationship identifies items that can aid understanding of the larger framework, perhaps contributing to identification of query terms or revision of search strategies. An example would be a segment in which an interviewee describes activities like activities described in a topic description, but which occurred at a different place or time than the topic description
Pointer
Provides pointers to a source of more information. This could be a person, group, another segment, etc
•Pointers will be segments that provide suggestions or explicit evidence of where to find more relevant information. An example of a pointer segment would be one in which an interviewee identifies another interviewee who had personal experiences directly associated with the topic. The value of these segments is in identifying other relevant segments, particularly but not limited to segments about a topic.
Quality Assurance
• 20 topics were redone, 10 were reviewed.
• Redo: A second assessor did a topic from scratch
• Review: A second assessor reviewed the first assessors work and did additional searches when needed.
• Assessors would then get together and discuss their interpretation of the topic and resolved differences in relevance judgments.
• Assessors kept notes on the process.
Looking Forward: 2006
• Working systems in five languages– Real users searching real data
• Rich experience beyond broadcast news– Frameworks, components, systems
• Affordable application-tuned systems– Oral history, lectures, speeches, meetings, …
For More Information
• The MALACH project– http://www.clsp.jhu.edu/research/malach/
• NSF/EU Spoken Word Access Group– http://www.dcs.shef.ac.uk/spandh/projects/swag/
• Speech-based retrieval– http://www.glue.umd.edu/~dlrg/speech/