1. IBM India Research Laboratory Overview
IBM India Research Laboratory Overview with an effort to be in the context of FIRE
Debapriyo Majumdar
IBM India Research Lab, Bangalore
December 14, 2008

2. IBM Research - Overview
The largest private research institution in the world
Annual R&D budget of around $6B (includes development as well)
Over 3,000 researchers
Mathematics, Computer Science, Physics, Service Science, …
Over 40,000 US patents since 1993
Most patents of all companies in the world in the last 15 years
Eight labs across the world
December 14, 2008

3. IBM Research Labs Worldwide
Established: 1995
Austin
Beijing
Zürich
Established: 1956
Almaden
Established: 1986
Established: 1952
San Jose
Watson
Established: 1945
Columbia University
Watson
Established: 1961
Established: 1982
Tokyo
Established: 1972
Haifa
Established:1998/2005
India–DEL/BLR
December 14, 2008

4. IBM India and India Research Lab
IBM India - Second largest population of IBM outside the US (over 75,000)
Current technical population 40,000+
India Research Lab
Delhi, since 1998
Bangalore, since 2005
About 150 technical people .
Delhi .
Kolkata . . .
Mumbai
Pune
Hyderabad
IBM India Business Units
Application Services
Business Process Transformation Services
India Software Lab (ISL)
Global Service Delivery Center
India Research Lab (IRL)
Domestic Operations/Others . .
Chennai
Bangalore
December 14, 2008

5. Technical Competencies
IRL Focus Areas
Business Areas
Service Delivery
Emerging Solutions
Software
Infrastructure
Services
Application
Services
Contact
Center
Services
Telecom
Others
(Banking,
etc.)
Systems
Technical Competencies
Computer Science
Distributed Systems – systems mgmt., middleware
Information Management – IE, Data mining
Interaction Technologies – speech
Programming Technologies – parallel and hi-perf. prog.
Software Engineering – model-driven, distributed dev.
Math Science
Operations Research
Algorithms
Optimization
Game Theory
Service Science
Service Engineering
Service Productivity
Service Management
Service Quality
Service Supply Chains
December 14, 2008

6. Why do we care? Services dominate the world’s GDP…
United States
Japan
India
China
Thailand 2003
Agr, Ind, Svcs (Distr, Other)
GDP 10, 38, 14, 38
Labor 40, 16, 15, 28
December 14, 2008

7. Technical Competencies
IRL Focus Areas
Business Areas
Service Delivery
Emerging Solutions
Software
Infrastructure
Services
Application
Services
Contact
Center
Services
Telecom
Others
(Banking,
etc.)
Systems
In the context of FIRE
Technical Competencies
Computer Science
Distributed Systems – systems mgmt., middleware
Information Management – IE, Data Mining
Interaction Technologies – speech
Programming Technologies – parallel and hi-perf. prog.
Software Engineering – model-driven, distributed dev.
Math Science
Operations Research
Algorithms
Optimization
Game Theory
Service Science
Service Engineering
Service Productivity
Service Management
Service Quality
Service Supply Chains
December 14, 2008

8. Information and Knowledge Management @ IRL
Speech recognition and synthesis
Hindi, Indian English & Hinglish
Translation: Hindi  English and English  Hindi
UIMA Annotators (rule based)
with IIT-Bombay
Linking structured and unstructured data
Learning attributes from noisy or incomplete information
For example, customer transaction logs
More…
December 14, 2008

9. Challenges Data Problem Focus on the application Noisy Incomplete
Could be ill-structured
Problem
Defining the problem is often our job too
Focus on the application
What you build must work
Users must be satisfied
Firefighting
December 14, 2008

10. Some Examples…
December 14, 2008

11. Speech - Core Technologies
Desktop speech recognition (Hindi & Indian English)
More than 1100 speakers
More than 250 hours of broadband speech data
Vocabulary of words
Accuracy: 90-95%
Telephony speech recognition
500 speakers each for Hindi, English & Hinglish
A prototype for movie booking system in Indian English
December 14, 2008

12. SENSEI: Voice and Accent Training for Call-Centers
Challenges
Increase in the number of call centers in India
Agents need to speak in foreign accent
Very high attrition rates in call-centers
Hiring involves evaluation and training
Solution: Sensei, a tool that is used for:
Candidate Screening: evaluates a candidate’s pronunciation, grammar and fluency
On-board Training: evaluates correctness of sounds produced, syllable stress, speaking rate and fluency
Monitoring: analyzes pre-recorded calls to determine if the agent maintained the required quality of voice/accent
Application: Cost reduction by automation of Accent Training and Evaluation
The Hindu, 30 Oct. 2006
December 14, 2008

13. Machine Translation: Linguistic & Statistical
December 14, 2008

14. English-Hindi Machine Translation system
December 14, 2008

15. Speech Recognition in IBM-IRL
Nitendra Rajput, “Statistical Language Modeling for Hindi Speech Recognition” National Symposium on Modelling and Shallow Parsing of Indian Languages, MSPIL 2006.
M Kumar, N Rajput, A Verma, “Hybrid Baseform Builder for Phonetic Languages,” International Conference on Intelligent Sensor and Information Processing, Jan 2005, Chennai.
Mohit Kumar, Nitendra Rajput, Ashish Verma, “A large-vocabulary continuous speech recognition system for Hindi,” IBM Journal of Research and Development, Vol. 48, No. 5/6, 2004.
Nitendra Rajput, L. Venkata Subramaniam, Ashish Verma, Adapting Phonetic Decision Trees Between Languages for Continuous Speech Recognition,” Proceedings: IEEE International Conference on Spoken Language Processing (ICSLP 2000), Beijing, China, Oct 16-20, 2000.
Niloy Mukherjee, Nitendra Rajput, L. Venkata Subramaniam, Ashish Verma, On Deriving a Phoneme Model for a New Language,” Proceedings: IEEE International Conference on Spoken Language Processing (ICSLP 2000), Beijing, China, Oct 16-20, 2000.
Raghavendra Udupa U, Tanveer A Faruquie, Hemanta K Maji, "An algorithmic framework for the decoding problem in statistical machine translation," COLING 2004.
R. Udupa and T. Faruquie, "An english-hindi statistical machine translation system," in Proceedings of the 1st IJCNLP, Sanya, Hainan Island, China.
Tanveer Faruquie, Nitendra Rajput, Vimal Raj, “Improving automatic call classification using machine translation,” IEEE ICASSP 2007, Honolulu, Hawai, USA, Jan 2007.
December 14, 2008

16. EROCS: Entity RecOgnition in Context of Structured data
Extracted entities and
keywords/features
Original text
Complain:
I have noticed in my statement that you have deducted Rs750 from my a/c (# ) as account maintenance fee. Can you please explain why you have charged this money?
I have noticed in my statement that you have deducted Rs750 from my a/c (# ) as account maintenance fee. Can you please explain why you have charged this money?
CustID:
SavingID:
Unhappy,
Simple Saving A/C
Complaint metadata + Customer/account data brought together by automatically linking the complaint with the customer/account
Exploit linked information analysis in core business
Up-sell/Cross-sell, customer segmentation, campaign assessment, churn analysis.
December 14, 2008

17. EROCS: Entity RecOgnition in Context of Structured data
Linkage Discovery
December 14, 2008

18. CallAssist Customer to Agent: Hi, I am John ….
Transcript of Call
Customer to Agent: Hi, I am John ….
….. status of a DVD player ….
…….
Agent to Customer: …tell me the brand…?
Customer to Agent: …… I bought a Sony….
Present relevant transaction data and follow-up question to the agent within seconds
Consistent, high-quality customer experience
Reduce agent training cost
Reduces privacy concerns
Customer
StoreId
Product
Brand
John Smith
S8976
DVD Player LG
John Parker
Sony
December 14, 2008

19. FIRE: It has been a great start!
That’s all for now…
IBM Research – India
Technical areas, applications, Services
Examples on:
Speech related works…
Translation…
Information Extraction…
FIRE: It has been a great start!
December 14, 2008

20. Thank you!
December 14, 2008