1. Searching and Summarizing Speech
Julia Hirschberg
CS 6998
11/28/2018

2. Today Speech browsing and search Speech summarization: 2 views
Hori et al
Barzilay et al
Speech data mining
11/28/2018

3. Searching Audio Data
Today, large amounts of audio data available: on the web, in company archives, in our homes
But what can we do with it?
We have tools supporting random access to text – but for audio we’re limited to serial search
Goal: tools to search audio as easily as text
11/28/2018

4. Why? Searching online news and archives Searching a/v archives, movies
Searching trial recordings and legislative sessions
Browsing meetings, customer care exchanges, focus groups
Telephone calls and voic
11/28/2018

5. Audio Browsing/Retrieval for Voicemail
Motivated by interviews, surveys and usage logs of heavy users:
Hard to scan new msgs to find those you need to deal with quickly
Hard to find msg you want in archive
Hard to locate information you want in any msg
How could we help?
Increasing amounts of audio data available in corporate, public and private collections – but useless without tools for searching, browsing
SCAN and SCANMail prototypes: tools for searching and browsing speech data in Broadcast News and voic domains
Future applications: customer care, conference call and meeting browsing
11/28/2018

6. SCANMail Architecture
Caller
SCANMail Architecture
SCANMail
Subscriber

7. Corpus Collection
Recordings collected from 138 AT&T Labs employees’ mailboxes
100 hours; 10K msgs; 2500 speakers
Gender balanced: 12% non-native speakers
Mean message duration 36.4 secs, median 30.0 secs
Hand-transcribed and annotated with caller id, gender, age, entity demarcation (names, dates, telnos)
11/28/2018

8. Transcription and Bracketing
[ Greeting: hi R ] [ CallerID: it's me ] give me a call [ um ] right away cos there's [ .hn ] I guess there's some [ .hn ] change [ Date: tomorrow ] with the nursery school and they [ um ] [ .hn ] anyway they had this idea [ cos ] since I think J's the only one staying [ Date: tomorrow ] for play club so they wanted to they suggested that [ .hn ] well J2 actually offered to take J home with her and then would she
11/28/2018

9. would meet you back at the synagogue at [ Time: five thirty ] to pick her up [ .hn ] [ uh ] so I don't know how you feel about that otherwise M_ and one other teacher would stay and take care of her till [ Date: five thirty tomorrow ] but if you [ .hn ] I wanted to know how you feel before I tell her one way or the other so call me [ .hn ] right away cos I have to get back to her in about an hour so [ .hn ] okay [ Closing: bye [ .nhn ] [ .onhk ]
11/28/2018

10. Audix password: (null)
SCANMail Demo
Audix extension: 8380
Audix password: (null)
11/28/2018

11. Information Extraction from Speech
Jansche & Abney ‘02
11/28/2018

12. Speech Summarization: Extraction Techniques
Hori et al ‘02
Inoue et al ‘04
11/28/2018

13. Domain Specific Summarization (Barzilay et al ‘00)
Motivation: lab experiments show little facilitation of speech summarization by techniques that do improve search
Domain: Broadcast News
Idea: knowing what type of speaker (anchor, reporter, interviewee) is speaking provides structural clues that can “outline” the newscast since programs are predictable
11/28/2018

14. SCAN: Spoken Content-based Audio Navigator
TREC SDR corpus of Broadcast News
Segment speech `documents’ into audio `paratones’ acoustically
Segmentation module trained on hand-labeled discourse structure annotation in another domain
Classify recording conditions, e.g.
Music, telephone bandwidth, wide-band
Run ASR with appropriate acoustic models (~70% wac)
Index (errorful) transcripts using SMART IR
11/28/2018

15. Transcript prosodically formatted Overview provides abstract structure
Results in WYSIAWY (“What you see is almost what you hear”) GUI
Transcript prosodically formatted
Overview provides abstract structure
11/28/2018

16. SCAN db Acoustic Condition Classification Paratone Detector
Recognition
SCAN db
Broadcast News
corpus
Information
Retrieval
GUI
11/28/2018

17. Search
Overview
Transcript
11/28/2018

18. Patterns in Newscasts Anchors present headlines and introduce stories
Most frequent speakers
Anchor/reporter turn alternation
Reporter/guest turntaking during stories
11/28/2018

19. Data 35 broadcasts of “All Things Considered”
Human and ASR transcripts (without commercials but with turn boundaries)
Features to predict speaker role
Lexical: ngrams 1-5, explicit introductions (current and prior segment)
Contextual: labels and features of prior turns
Durational: turn length (absolute and relative to previous)
11/28/2018

20. Methods and Results
Boosting and maximum entropy --> simple weighted rules to predict speaker role
Baseline: guess anchor (35.4%)
Result on human transcripts:
BoostTexter 79%
MaxEnt 80.5%
Result on ASR transcripts:
BoostTexter 72.8%
MaxEnt 77%
11/28/2018

21. Speech Data Mining How does it differ from text data mining?
Maskey et al ‘04
11/28/2018