When building voice-enabled chatbots with Amazon Lex, one of the biggest challenges is accurately capturing user speech input for slot values. For example, when a user needs to provide their account number or confirmation code, speech recognition accuracy becomes crucial. This is where transcription confidence scores come in to help ensure reliable slot filling.
What Are Transcription Confidence Scores?
Transcription confidence scores indicate how confident Amazon Lex is in converting speech to text for slot values. These scores range from low to high and are separate from intent/entity recognition scores. For each spoken slot value, Lex provides a confidence score that you can use to:
- Validate if a spoken slot value was correctly understood
- Decide whether to ask for confirmation or re-prompt
- Branch conversation flows based on recognition confidence
Here are some ways to leverage confidence scores for better slot handling:
- Progressive Confirmation
- High confidence (>0.9): Accept the slot value and continue
- Medium confidence (0.6-0.9): Ask user to confirm (“Did you say 12345?”)
- Low confidence (<0.6): Re-prompt for the slot value
- Adaptive re-prompting
- Customize re-prompt messages based on confidence level
- Provide more specific guidance for low confidence inputs
- Offer alternative input methods when needed
- Branching Logic
- Route to human agent if multiple low confidence attempts
- Skip confirmation for consistently high confidence inputs
- Adjust validation rules based on confidence thresholds
The post includes an AWS CloudFormation template, to demonstrate these patterns, that you can deploy in your AWS account.
Using confidence scores strategically helps create more robust slot filling experiences that:
- Reduce errors in capturing critical information
- Improve containment rates for self-service
- Provide better handling of challenging audio conditions
- Enable smarter conversation flows
By implementing these patterns, you can significantly improve the accuracy and reliability of slot filling in your Lex voice bots while maintaining a natural conversation flow.
Solution overview
The following diagram illustrates the overall architecture for the solution.
We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
Amazon Lex asks the caller “In a few words, how can I help you today?” Amazon Lex reviews the transcription confidence score of the slot values to influence the confirmation routine.
Interaction model
The following are different customer experiences a caller may have depending on what they say and the transcription confidence scores returned by Amazon Lex:
Virtual Agent:
Thank you for calling Acme Travel.
Virtual Agent:
…In a few words, what is the reason for your call today?
Customer:
I’d like to check my booking.
Virtual Agent:
That’s great, please say your 5 character booking reference, you will find it at the top of the information pack we sent.
Customer:
1 9 A K B (Recognized with low transcription confidence: 0.4)
Virtual Agent:
Ok, let’s try again. What is your booking reference?
Customer:
1 9 A A B (More clearly pronounced: recognized with 0.8)
Virtual Agent:
Your booking 1 9 A A B is currently being progressed.
Virtual Agent:
Thank you for calling Acme Travel.
Virtual Agent:
…In a few words, what is the reason for your call today?
Customer:
I’d like to check my booking 1 9 A Z Y.
Virtual Agent:
Your booking 1 9 A Z Y is currently being progressed.
Virtual Agent:
Thank you for calling Acme Travel.
Virtual Agent:
…In a few words, what is the reason for your call today?
Customer:
I’d like to check my booking.
Virtual Agent:
That’s great, please say your 5 character booking reference, you will find it at the top of the information pack we sent.
Customer:
1 9 A Z Y (recognized with a transcription confidence of 0.6)
Virtual Agent:
Did you say 1 9 A Z Y? Please say yes or no.
Customer:
Yes
Virtual Agent:
Your booking 1 9 A Z Y is currently being progressed.
In the example conversations, the IVR requests the booking reference from the customer. Once received, the transcription confidence score is evaluated by enabling conditional branching in Amazon Lex based on speech confidence scores. These conditions check the value against specific thresholds. If the transcription confidence score exceeds the high threshold (for example, greater than 0.7), the conversation progresses to the next state. If the score falls in the medium confidence range (for example, between 0.4–0.7), the user is asked to confirm the interpreted input. Finally, if the score falls below a minimum threshold (for example, lower than 0.4), the user is prompted to retry and provide the information again. This approach optimizes the conversation flow based on the quality of the input captured and prevents erroneous or redundant slot capturing, leading to an improved user experience while increasing the self-service containment rates.
Prerequisites
You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. If you don’t have an AWS account, see How do I create and activate a new Amazon Web Services account?
Additionally, you need an Amazon Connect instance—you use the instance Amazon Resource Name (ARN) in a later step.
Deploy the Amazon Lex bot and Amazon Connect flow
To create the sample bot and configure the runtime phrase hints, perform the following steps. For this example, we create an Amazon Lex bot called disambiguation-bot, one intent (CheckBooking), and one slot type (BookingRef).
- Sign in to your AWS account, then choose Launch Stack to deploy the CloudFormation template:
- For Stack Name, enter a name, for example
contact-center-transcription-confidence-scores. - Choose Next.
- Provide the following parameters:
- For BotName, enter disambiguation-bot.
- For ConnectInstanceARN, enter the ARN of your Amazon Connect instance.
- For ContactFlowName, enter a name for your Amazon Connect contact flow (for example,
lex-check-booking-sample-flow). - For LogGroupName, enter the name of the Amazon CloudWatch log group where the conversation logs are stored.
- Choose Next.
- Leave all remaining settings as default and choose Next.
- Select I acknowledge that AWS CloudFormation might create IAM resources.
- Choose Submit.
- Wait for the CloudFormation stack to successfully deploy.
- On the Amazon Connect console, assign the contact flow to an Amazon Connect claimed number.
Configure the transcript confidence score logic
After you create your intent (CheckBooking), use you can Visual conversation builder to configure your transcription confidence score logic.
The following figure is an example of how we add logic to the intent. Highlighted in red is the branch condition where we use the transcription confidence score to dynamically change the customer experience and improve accuracy.
If you choose the node, you’re presented with the following configuration options, which is where you can configure the branch condition.
Test the solution
To test the solution, we examine a conversation with words that might not be clearly understood.
- Assign the Amazon Lex bot to an Amazon Connect workflow.
- Make a call.
Amazon Connect will ask “Thank you for calling Acme travel, In a few words, what is the reason for your call today?”
- Respond “I want to check my booking.”
- When asked for the booking reference, speak any two numbers followed by three letters (for example, “1 9 A Z Y”).
This test checks the confidence score and will either say “your booking 1 9 A Z Y is currently being progressed” or it will ask you to confirm “1 9 A Z Y”.
Limitations
Audio transcription confidence scores are available only in the English (GB) (en_GB) and English (US) (en_US) languages. Confidence scores are supported only for 8 kHz audio input. Transcription confidence scores aren’t provided for audio input from the test window on the Amazon Lex V2 console because it uses 16 kHz audio input.
Clean up
To remove the infrastructure created by the CloudFormation template, open the AWS CloudFormation console and delete the stack. This will remove the services and configuration installed as part of this deployment process.
Conclusion
Optimizing the user experience is at the forefront of any Amazon Lex conversational designer’s priority list, and so is capturing information accurately. This new feature empowers designers to have choices around confirmation routines that drive a more natural dialog between the customer and the bot. Although confirming each input can slow down the user experience and cause frustration, failing to confirm when transcription confidence is low can risk accuracy. These improvements enable you to create a more natural and performant experience.
For more information about how to build effective conversations on Amazon Lex with intent confidence scores, see Build more effective conversations on Amazon Lex with confidence scores and increased accuracy.
About the Authors
Alex Buckhurst is a Senior Amazon Connect consultant at Amazon Web Services with a focus on innovation and building customer-centric designs. In his downtime, Alex enjoys playing squash, perfecting his BBQ skills, and cherishing moments with his family.
Kai Loreck is a Senior professional services Amazon Connect consultant. He works on designing and implementing scalable customer experience solutions. In his spare time, he can be found playing sports, snowboarding, or hiking in the mountains.
Neel Kapadia is a Senior Software Engineer at AWS where he works on designing and building scalable AI/ML services using Large Language Models and Natural Language Processing. He has been with Amazon for over 5 years and has worked on Amazon Lex and Amazon Bedrock. In his spare time, he enjoys cooking, reading, and traveling.
Anand Jumnani is a DevOps Consultant at Amazon Web Services based in United Kingdom. Outside of work, he is passionate about club cricket and enjoys spending quality time with family and friends.
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