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1 August 9, 20061 David Claiborn SLM Tuning: Lessons Learned.

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Presentation on theme: "1 August 9, 20061 David Claiborn SLM Tuning: Lessons Learned."— Presentation transcript:

1 1 August 9, David Claiborn SLM Tuning: Lessons Learned

2 August 9,  Define Statistical Language Model (SLM)  Advantages and disadvantages of SLM technology  How an SLM is used at Sprint/Nextel  Practical SLM Tuning considerations  Is an SLM right for your speech project?  Questions Agenda:

3 August 9, What is the Statistical Language Model? Definition of an SLM: A statistical language model is a probabilistic description of the constraints on word order found in a given language.(Bahl et al 1983)  For our purposes think of an SLM as the probability of utterances occurring in a particular dialog state. This probability is created from caller utterances captured in that specific dialog state.  Traditionally, SLM technology is employed to give callers the ability to make requests using natural or conversational speech. SLM dialog states are often referred to as “Say Anything” states.

4 August 9, Advantages and Disadvantages of an SLM over a finite state grammar Advantages:  Flexibility to callers  Able to serve natural speech requests  Minimized need for guidance from prompting Disadvantages:  Difficult to train and update  Transcription must be even more precise  Cost  Time

5 August 9, SLM Application at Sprint/Nextel  At Sprint/Nextel the SLM is literally the front door into the IVR.  In the diagram below we can see the SLM offers unique treatment to seven different “phone” centered requests. SLM/ SayAnything MainMenu General Support Agent Handset Support App Handset Sales Agent Lost Phone Accessories App Broken Phone Agent Technical Issues App “My phone won’t make calls.” “Phone” “I have a question about my phone.” “I want to buy a new phone.” “I lost my phone.”“I want to buy a phone charger.” “My phone is broken.”

6 August 9, Things to consider when Tuning an SLM:  Does the SLM need a new destination or training to fulfill design requirements?  When training the SLM, what is a statistically relevant number of utterances to train on?  Do I have the expertise to tune this Say Anything state in house?  Do I have quality transcription in place? Have they guaranteed to maintain a certain level of accuracy (above 98%)?  Have I established a baseline to judge post tuning improvement?

7 August 9, Is an SLM right for your speech project?  How many applications does the Customer Care IVR have today and what additional apps do you hope to add in the next five years.  How many callers enter the Customer Care IVR in a give year, what are the high and low months and are there certain months or times of each month where certain requests increase?  What level of call routing granularity are you looking to accomplish?  How rapidly will this system need to be taking calls?  What are your goals; increased CSAT and Call Completions, decreased agent to agent transfers? At these were the initial questions IBM Global Services asked Sprint which led to the creation of Sprint’s SLM:

8 August 9, Questions?

9 August 9, Bibliography: Bahl, L.R., Jelinek, F. & Mercer, R.L. (1983) "A Maximum Likelihood Approach to Continuous Speech Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, 5 (2), pp

10 August 9, David Claiborn VUI Designer and Tuner


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