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Simplify patient care with a custom voice assistant using Amazon Lex V2

For the past few decades, physician burnout has been a challenge in the healthcare industry. Although patient interaction and diagnosis are critical aspects of a physician’s job, administrative tasks are equally taxing and time-consuming. Physicians and clinicians must keep a detailed medical record for each patient. That record is stored in the hospital electronic health…



For the past few decades, physician burnout has been a challenge in the healthcare industry. Although patient interaction and diagnosis are critical aspects of a physician’s job, administrative tasks are equally taxing and time-consuming. Physicians and clinicians must keep a detailed medical record for each patient. That record is stored in the hospital electronic health record (EHR) system, a database that contains the records of every patient in the hospital. To maintain these records, physicians often spend multiple hours each day to manually enter data into the EHR system, resulting in lower productivity and increased burnout.

Physician burnout is one of the leading factors that lead to depression, fatigue, and stress for doctors during their careers. In addition, it can lead to higher turnover, reduced productivity, and costly medical errors, affecting people’s lives and health.

In this post, you learn the importance of voice assistants and how they can automate administrative tasks for doctors. We also walk through creating a custom voice assistant using PocketSphinx and Amazon Lex.

Voice assistants as a solution to physician burnout

Voice assistants are now starting to automate the vital yet manual parts of patient care. They can be a powerful tool to help doctors save time, reduce stress, and spend more time focusing on the patient versus the administrative requirements of clinical documentation.

Today, voice assistants are becoming more available as natural language processing models advance, errors decrease, and development becomes more accessible for the average developer. However, most devices are limited, so developers must often build their own customized versions.

As Solutions Architects working in the healthcare industry, we see a growing trend towards the adoption of voice assistants in hospitals and patient rooms.

In this post, you learn how to create a custom voice assistant using PocketSphinx and Amazon Lex. With our easy-to-set-up and managed services, developers and innovators can hit the ground running and start developing the devices of the future.

Custom voice assistant solution architecture

The following architecture diagram presents the high-level overview of our solution.

In our solution, we first interface with a voice assistant script that runs on your computer. After the wake word is recognized, the voice assistant starts recording what you say and sends the audio to Amazon Lex, where it uses an AWS Lambda function to retrieve dummy patient data stored in Amazon DynamoDB. The sensor data is generated by another Python script,, which you also run on your computer.

Sensor types include blood pressure, blood glucose, body temperature, respiratory rate, and heart rate. Amazon Lex sends back a voice message, and we use Amazon Polly, a service that turns text into lifelike speech, to create a consistent experience.

Now you’re ready to create the components needed for this solution.

Deploy your solution resources

You can find all the files of our custom voice assistant solution on our GitHub repo. Download all the files, including the PocketSphinx model files downloaded from their repo.

You must deploy the DynamoDB table and Lambda function directly by choosing Launch Stack.

The AWS CloudFormation stack takes a few minutes to complete. When it’s complete, you can go to the Resources tab to check out the Lambda function and DynamoDB table created. Note the name of the Lambda function because we reference it later when creating the Amazon Lex bot.

Create the Amazon Lex bot

When the CloudFormation stack is complete, we’re ready to create the Amazon Lex bot. For this post, we use the newer V2 console.

  1. On the Amazon Lex console, choose Switch to the new Lex V2 console.
  2. In the navigation pane, choose Bots.
  3. Choose Create bot.
  4. For Bot name, enter Healthbot.
  5. For Description, enter an optional description.
  6. For Runtime role, select Create a role with basic Amazon Lex permissions.
  7. In the Children’s Online Privacy Protection Act (COPPA) section, select No.
  8. Keep the settings for Idle session timeout at their default (5 minutes).
  9. Choose Next.

  1. For Voice interaction, choose the voice you want to use.
  2. Choose Done.

Create custom slot types, intents, and utterances

Now we create a custom slot type for the sensors, our intents, and sample utterances.

  1. On the Slot types page, choose Add slot type.
  2. Choose Add blank slot type.
  3. For Slot type name¸ enter SensorType.
  4. Choose Add.
  5. In the editor, under Slot value resolution, select Restrict to slot values.

  1. Add the following values:
    1. Blood pressure
    2. Blood glucose
    3. Body temperature
    4. Heart rate
    5. Respiratory rate

  1. Choose Save slot type.

On the Intents page, we have two intents automatically created for us. We keep the FallbackIntent as the default.

  1. Choose NewIntent.
  2. For Intent name, change to PatientData.

  1. In the Sample utterances section, add some phrases to invoke this intent.

We provide a few examples in the following screenshot, but you can also add your own.

  1. In the Add slot section, for Name, enter PatientId.
  2. For Slot type¸ choose AMAZON.AlphaNumeric.
  3. For Prompts, enter What is the patient ID?

This prompt isn’t actually important because we’re using Lambda for fulfillment.

  1. Add another required slot named SensorType.
  2. For Slot type, choose SensorType (we created this earlier).
  3. For Prompts, enter What would you like to know?
  4. Under Code hooks, select Use a Lambda function for initialization and validation and Use a Lambda function for fulfillment.

  1. Choose Save intent.
  2. Choose Build.

The build may take a few minutes to complete.

Create a new version

We now create a new version with our new intents. We can’t use the draft version in production.

  1. When the build is complete, on the Bot versions page, choose Create version.
  2. Keep all the settings at their default.
  3. Choose Create.

You should now see Version 1 listed on the Bot Versions page.

Create an alias

Now we create an Alias to deploy.

  1. Under Deployment in the navigation pane, choose Aliases.
  2. Chose Create alias.
  3. For Alias name¸ enter prod.
  4. Associate this alias with the most recent version (Version 1).

  1. Choose Create.
  2. On the Aliases page, choose the alias you just created.
  3. Under Languages, choose English (US).

  1. For Source, choose the Lambda function you saved earlier.
  2. For Lambda function version or alias, choose $LATEST.

  1. Choose Save.

You now have a working Amazon Lex Bot you can start testing with. Before we move on, make sure to save the bot ID and alias ID.

The bot ID is located on the bot details page.

The alias ID is located on the Aliases page.

You need to replace these values in the voice assistant script later.

In the following sections, we explain how to use PocketSphinx to detect a custom wake word as well as how to start using the solution.

Use PocketSphinx for wake word recognition

The first step of our solution involves invoking a custom wake word before we start listening to your commands to send to Amazon Lex. Voice assistants need an always on, highly accurate, and small footprint program to constantly listen for a wake word. This is usually because they’re hosted on a small, low battery device such as an Amazon Echo.

For wake word recognition, we use PocketSphinx, an open-source continuous speech recognition engine made by Carnegie Mellon University, to process each audio chunk. We decided to use PocketSphinx because it provides a free, flexible, and accurate wake system with good performance.

Create your custom wake word

Building the language model using PocketSphinx is simple. The first step is to create a corpus. You can use the included model that is pre-trained with “Amazon” so if you don’t want to train your own wake word, you can skip to the next step. However, we highly encourage you to test out creating your own custom wake word to use with the voice assistant script.

The corpus is a list of sentences that you use to train the language model. You can find our pre-built corpus file in the file corpus.txt that you downloaded earlier.

  1. Modify the corpus file based on the key phrase or wake word you want to use and then go to the LMTool page.
  2. Choose Browse AND select the corpus.txt file you created
  4. Download the files the tool created and replace the example corpus files that you downloaded previously.
  5. Replace the KEY_PHRASE and DICT variables in the Python script to reflect the new files and wake word.

  1. Update the bot ID and bot alias ID with the values you saved earlier in the voice assistant script.

Set up the voice assistant script on your computer

In the GitHub repository, you can download the two Python scripts you use for this post: and

You must complete a few steps before you can run the script, namely installing the correct Python version and libraries.

  1. Download and install Python 3.6.

PocketSphinx supports up to Python 3.6. If you have another version of Python installed, you can use pyenv to switch between Python versions.

  1. Install Pocketsphinx.
  2. Install Pyaudio.
  3. Install Boto3.

Make sure you use the latest version by using pip install boto3==.

  1. Install the AWS Command Line Interface (AWS CLI) and configure your profile.

If you don’t have an AWS Identity and Access Management (IAM) user yet, you can create one. Make sure you set the Region to the same Region where you created your resources earlier.

Start your voice assistant

Now that we have everything set up, open up a terminal on your computer and run

Make sure to run it for at least a minute so that the table is decently populated. Our voice assistant only queries the latest data inserted into the table, so you can stop it after it runs one time. The patient IDs generated are between 0–99, and are asked for later.

Check the table to make sure that data is generating.

Now you can run

Your computer is listening for the wake word you set earlier (or the default “Amazon”) and doesn’t start recording until it detects the wake word. The wake word detection is processed using PocketSphinx’s decoder. The decoder continuously checks for the KEYPHRASE or WakeWord in the audio channel.

To initiate the conversation, say the utterance you set in your intent earlier. The following is a sample conversation:

You: Hey Amazon

You: I want to get patient data.

Lex: What is the ID of the patient you wish to get information on?

You: 45

Lex: What would you like to know about John Smith?

You: blood pressure

Lex: The blood pressure for John Smith is 120/80.


Congratulations! You have set up a healthcare voice assistant that can serve as a patient information retrieval bot. Now you have completed the first step towards creating a personalized voice assistant.

Physician burnout is an important issue that needs to be addressed. Voice assistants, with their increasing sophistication, can help make a difference in the medical community by serving as virtual scribes, assistants, and much more. Instead of burdening physicians with menial tasks such as ordering medication or retrieving patient information, they can use innovative technologies to relieve themselves of the undifferentiated administrative tasks.

We used PocketSphinx and Amazon Lex to create a voice assistant with the simple task of retrieving some patient information. Instead of running the program on your computer, you can try hosting this on any small device that supports Python, such as the Raspberry Pi.

Furthermore, Amazon Lex is HIPAA-eligible, which means that you can integrate it with existing healthcare systems by following the HL7/FHIR standards.

Personalized healthcare assistants can be vital in helping physicians and nurses care for their patients, and retrieving sensor data is just one of the many use cases that can be viable. Other use cases such as ordering medication and scribing conversations can benefit doctors and nurses across hospitals.

We want to challenge you to try out Amazon Lex and see what you can make!

About the Author

David Qiu is a Solutions Architect working in the HCLS sector, helping healthcare companies build secure and scalable solutions in AWS. He is passionate about educating others on cloud technologies and big data processing. Outside of work, he also enjoys playing the guitar, video games, cigars, and whiskey. David holds a Bachelors in Economics and Computer Science from Washington University in St. Louis.



Manish Agarwal is a technology enthusiast having 20+ years of engineering experience ranging from leading cutting-edge Healthcare startup to delivering massive scale innovations at companies like Apple and Amazon. Having deep expertise in AI/ML and healthcare, he truly believes that AI/ML will completely revolutionize the healthcare industry in next 4-5 years. His interests include precision medicine, Virtual assistants, Autonomous cars/ drones, AR/VR and blockchain. Manish holds Bachelors of Technology from Indian Institute of Technology (IIT).


Navneet Srivastava, a Principal Solutions Architect, is responsible for helping provider organizations and healthcare companies to deploy data lake, data mesh, electronic medical records, devices, and AI/ML-based applications while educating customers about how to build secure, scalable, and cost-effective AWS solutions. He develops strategic plans to engage customers and partners, and works with a community of technically focused HCLS specialists within AWS. Navneet has a M.B.A from NYIT and a bachelors in Software Engineering and holds several associate and professional certifications for architecting on AWS.


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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…




This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia


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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…




A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.


For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!


If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.


In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.


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Enhance the caller experience with hints in Amazon Lex

We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up…




We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up such clues or hints based on their domain knowledge and access to business intelligence dashboards. Amazon Lex now supports a hints capability to enhance the recognition of relevant phrases in a conversation. You can programmatically provide phrases as hints during a live interaction to influence the transcription of spoken input. Better recognition drives efficient conversations, reduces agent handling time, and ultimately increases customer satisfaction.

In this post, we review the runtime hints capability and use it to implement verification of callers based on their mother’s maiden name.

Overview of the runtime hints capability

You can provide a list of phrases or words to help your bot with the transcription of speech input. You can use these hints with built-in slot types such as first and last names, street names, city, state, and country. You can also configure these for your custom slot types.

You can use the capability to transcribe names that may be difficult to pronounce or understand. For example, in the following sample conversation, we use it to transcribe the name “Loreck.”

Conversation 1

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 1111 2222 3333 4444

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Loreck

IVR: Thank you. The balance on your checking account is 123 dollars.

Words provided as hints are preferred over other similar words. For example, in the second sample conversation, the runtime hint (“Smythe”) is selected over a more common transcription (“Smith”).

Conversation 2

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Smythe

IVR: Thank you. The balance on your checking account is 456 dollars.

If the name doesn’t match the runtime hint, you can fail the verification and route the call to an agent.

Conversation 3

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Savings

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Jane

IVR: There is an issue with your account. For support, you will be forwarded to an agent.

Solution overview

Let’s review the overall architecture for the solution (see the following diagram):

  • We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
  • We use a dialog codehook in the Amazon Lex bot to invoke an AWS Lambda function that provides the runtime hint at the previous turn of the conversation.
  • For the purposes of this post, the mother’s maiden name data used for authentication is stored in an Amazon DynamoDB table.
  • After the caller is authenticated, the control is passed to the bot to perform transactions (for example, check balance)

In addition to the Lambda function, you can also send runtime hints to Amazon Lex V2 using the PutSession, RecognizeText, RecognizeUtterance, or StartConversation operations. The runtime hints can be set at any point in the conversation and are persisted at every turn until cleared.

Deploy the sample Amazon Lex bot

To create the sample bot and configure the runtime phrase hints, perform the following steps. This creates an Amazon Lex bot called BankingBot, and one slot type (accountNumber).

  1. Download the Amazon Lex bot.
  2. On the Amazon Lex console, choose Actions, Import.
  3. Choose the file that you downloaded, and choose Import.
  4. Choose the bot BankingBot on the Amazon Lex console.
  5. Choose the language English (GB).
  6. Choose Build.
  7. Download the supporting Lambda code.
  8. On the Lambda console, create a new function and select Author from scratch.
  9. For Function name, enter BankingBotEnglish.
  10. For Runtime, choose Python 3.8.
  11. Choose Create function.
  12. In the Code source section, open and delete the existing code.
  13. Download the function code and open it in a text editor.
  14. Copy the code and enter it into the empty function code field.
  15. Choose deploy.
  16. On the Amazon Lex console, select the bot BankingBot.
  17. Choose Deployment and then Aliases, then choose the alias TestBotAlias.
  18. On the Aliases page, choose Languages and choose English (GB).
  19. For Source, select the bot BankingBotEnglish.
  20. For Lambda version or alias, enter $LATEST.
  21. On the DynamoDB console, choose Create table.
  22. Provide the name as customerDatabase.
  23. Provide the partition key as accountNumber.
  24. Add an item with accountNumber: “1111222233334444” and mothersMaidenName “Loreck”.
  25. Add item with accountNumber: “5555666677778888” and mothersMaidenName “Smythe”.
  26. Make sure the Lambda function has permissions to read from the DynamoDB table customerDatabase.
  27. On the Amazon Connect console, choose Contact flows.
  28. In the Amazon Lex section, select your Amazon Lex bot and make it available for use in the Amazon Connect contact flow.
  29. Download the contact flow to integrate with the Amazon Lex bot.
  30. Choose the contact flow to load it into the application.
  31. Make sure the right bot is configured in the “Get Customer Input” block.
  32. Choose a queue in the “Set working queue” block.
  33. Add a phone number to the contact flow.
  34. Test the IVR flow by calling in to the phone number.

Test the solution

You can now call in to the Amazon Connect phone number and interact with the bot.


Runtime hints allow you to influence the transcription of words or phrases dynamically in the conversation. You can use business logic to identify the hints as the conversation evolves. Better recognition of the user input allows you to deliver an enhanced experience. You can configure runtime hints via the Lex V2 SDK. The capability is available in all AWS Regions where Amazon Lex operates in the English (Australia), English (UK), and English (US) locales.

To learn more, refer to runtime hints.

About the Authors

Kai Loreck is a 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.

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Sravan Bodapati is an Applied Science Manager at AWS Lex. He focuses on building cutting edge Artificial Intelligence and Machine Learning solutions for AWS customers in ASR and NLP space. In his spare time, he enjoys hiking, learning economics, watching TV shows and spending time with his family.


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