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Extract Insights From Customer Conversations with Amazon Transcribe Call Analytics

In 2017, we launched Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to any application. Today, I’m very happy to announce the availability of Amazon Transcribe Call Analytics, a new feature that lets you easily extract valuable insights from customer conversations with a single API call. Each…

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In 2017, we launched Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to any application. Today, I’m very happy to announce the availability of Amazon Transcribe Call Analytics, a new feature that lets you easily extract valuable insights from customer conversations with a single API call.

Each discussion with potential or existing customers is an opportunity to learn about their needs and expectations. For example, it’s important for customer service teams to figure out the main reasons why customers are calling them, and measure customer satisfaction during these calls. Likewise, salespeople try to gauge customer interest, and their reaction to a particular sales pitch.

Thus, many customers and partners would like to add call analytics capabilities in different applications, regardless of their contact center provider. They often need to analyze more than phone calls, for example web-based audio and video calls. So far, they’ve typically done this by stitching AI services and dedicated ML models together, and they’ve asked us for a simpler solution.

We got to work and built Amazon Transcribe Call Analytics, a new addition to Transcribe and a key enhancement to AWS Contact Center Intelligence. If you can’t wait to try it, feel free to jump now to the AWS console. If you’d like to learn more, read on!

Introducing Amazon Transcribe Call Analytics
Based on ASR implemented in Transcribe, Transcribe Call Analytics adds natural language processing (NLP) capabilities specifically trained on customer calls, and optimized to provide highly accurate call transcripts and actionable insights. With a simple API call, developers can now easily add call analytics to any application, and extract customer insights from conversations without having to build AI pipelines and train custom ML models.

Key features of Transcribe Call Analytics include:

  • Timestamped turn-by-turn call transcription in 21 languages.
  • Issue detection, which picks up the shortest set of contiguous words in a conversation turn that represents the reason why the customer is calling. This works out of the box without any configuration or training.
  • Call categorization based on conversational characteristics:
    • Matching specific words and phrases,
    • Detecting non-talk time,
    • Detecting interruptions,
    • Analyzing sentiment for the customer and the agent.
  • Call characteristics such as:
    • How quickly and loudly a customer or agent are speaking,
    • Detecting non-talk time,
    • Detecting interruptions.
  • Redaction of sensitive data from the text transcript and the corresponding audio file.

For example, you can create rules to flag calls where customers interrupt the agent, exhibit negative sentiment, and say “I want to speak with the manager”. These calls certainly did not go well, and are worth analyzing in detail! You can also look for calls where agents don’t use pre-defined greetings (“Welcome to ACME Support, how can I help you today?”) within the first 15 seconds, to measure script compliance and help supervisors identify agent coaching opportunities. Another popular scenario is to create rules that flag mentions of your specific products and services (“Your ACME Turbo 2000 vacuum cleaner isn’t working like it should”), in order to pick up any emerging trends you’d need to be aware of.

Last but not least, you can further process the text transcript with other AI services such as Amazon Translate, or with custom NLP models built with Amazon SageMaker.

Now, let’s do a quick demo.

Extracting Insights with Amazon Transcribe Call Analytics
Here’s a fictitious support call, where a lady calls her bank to report that she’s lost her credit and debit cards. The sound file is a stereo WAV file (16-bit, 8KHz).

Transcribe Call Analytics requires that the agent and the customer are recorded in their own channel. We’ll also need to tell which is the agent channel. In a stereo file, the left channel is usually the first channel (channel #0), and the right channel is the second one (channel #1). This is the case for this call.

If you’re not sure which is which, you can easily use the versatile ffmpeg open source tool to extract each channel to a separate audio file.

$ ffmpeg -i demo-call.wav -map_channel 0.0.0 channel0.wav -map_channel 0.0.1 channel1.wav

You can use the same technique to extract audio channels from other file types, such as video files, and recombine them to a stereo audio file. You’ll find more information in the ffmpeg documentation.

Now that I’m sure that the agent is in channel #1, I use the AWS CLI to upload the audio file to an S3 bucket.

$ aws s3 cp launch-call.wav s3://jsimon-transcribe-useast1/demo-call.wav –region us-east-1

Opening the Transcribe Call Analytics console, I see that call category templates are available.

Call categories

I decide to create one for supervisor escalations. Then, with a couple of clicks, I create a custom call category named welcome-message, to check if the agent starts the call with an appropriate welcome. I could add several phrases to check for if needed. We recommend that you use short sentences to minimize the chance of filler words popping up (‘hmm’, ‘err’, and so on).

Call category

Then, I create a call analytics job using the general model available in Transcribe. I also enable automatic language detection.

Creating a job

Then, I define the location of the audio file in S3, flagging channel #1 as the agent channel.

Creating a job

I decide to store the transcript in the default S3 bucket created by Transcribe in my account. I could also use my own bucket if needed. Then, I pick an AWS Identity and Access Management (IAM) role with sufficient permissions, and I launch the job.

A minute later or so, the job is complete. The console contains a preview of the text transcript, as well as a link to the full JSON transcript.

Viewing the transcript

As the agent used the proper welcome sentence in the first 15 seconds, the call is tagged with the category I created earlier.

Call categories

Downloading the JSON transcript, each sentence in the conversation is enriched with metadata on per-word loudness, measured on a 0-100 range with 100 being extremely loud. Here’s the first sentence:

“BeginOffsetMillis”:440,”EndOffsetMillis”:4960,
“Sentiment”:”NEUTRAL”,
“ParticipantRole”:”AGENT”,
“LoudnessScores”:[78.68,80.4,81.91,78.95,82.34],
“Content”:”Hello and thank you for calling the bank. This is Ashley speaking, how may I help you today?”

Looking at the next sentence, I see that Transcribe Call Analytics automatically detected what the customer issue is. The corresponding text is in bold:

“Content”: “Hi um uh you just need to cancel my card. Um I have a debit card and a credit card.”,
“IssuesDetected”:[{“UnredactedCharacterOffsets”:{“Begin”: 26,”End”: 40}}. . .

At the end of the transcript, I see global call statistics (duration, talk time, words per minute, matched categories). Transcribe also gives me overall sentiment information, meaured from -5 (extremely negative) to +5 (extremely positive). I also get a a breakdown in four quarters.

“Sentiment”:{“OverallSentiment”:{“AGENT”:2.6,”CUSTOMER”:0.2},
“SentimentByPeriod”:{“QUARTER”:
{“AGENT”:[
{“Score”:1.9,”BeginOffsetMillis”:0,”EndOffsetMillis”:68457},
{“Score”:-0.7,”BeginOffsetMillis”:68457,”EndOffsetMillis”:136915},
{“Score”:5.0,”BeginOffsetMillis”:136915,”EndOffsetMillis”:205372},
{“Score”:3.0,”BeginOffsetMillis”:205372,”EndOffsetMillis”:273830}],
“CUSTOMER”:[
{“Score”:-1.7,”BeginOffsetMillis”:0,”EndOffsetMillis”:68165},
{“Score”:0.0,”BeginOffsetMillis”:68165,”EndOffsetMillis”:136330},
{“Score”:0.0,”BeginOffsetMillis”:136330,”EndOffsetMillis”:204495},
{“Score”:2.1,”BeginOffsetMillis”:204495,”EndOffsetMillis”:272660}]}}}

We can see that the customer started the call with negative sentiment, moving quickly to neutral sentiment, and ending the call with positive sentiment. This is a good sign that the call was handled satisfactorily, and that the customer problem was solved.

If you’d like to convert the transcript to a Word document with additional visualizations, my colleague Andrew Kane has built a nice tool and made it available on Github. Here’s a sample report produced by his tool.

Andrew's tool

AWS Customers and Partners Are Using Amazon Transcribe Call Analytics

Ben Rigby, the SVP, Global Head of Product & Engineering, Artificial Intelligence, Automation, and Workforce at Talkdesk told us, “Our customers are processing millions of customer service calls in their contact centers a year and have a critical need to extract actionable conversation insights to ensure positive business outcomes. As an AWS Contact Center Intelligence partner, we further enhanced our call transcription capabilities with Amazon Transcribe. With the launch of Amazon Transcribe Call Analytics, we’re excited to add even more AI capabilities to our Speech Analytics and QM Assist products. These deeper insights can provide agents and supervisors with the data they need to improve the speed and quality of their customer service while boosting workforce productivity.

Praphul Kumar, the Chief Product Officer of SuccessKPI adds, “Amazon Transcribe Call Analytics API enables us to add ML-based capabilities to our platform faster and at a lower cost. This new API removes the need to integrate multiple AI services together and develop custom machine learning models in certain areas. With Transcribe Call Analytics, we will be able to provide conversation insights such as sentiment, non-talk time, and call categories to gauge agent performance. This helps to drive better call outcomes, reduce agent turnover, uncover agent coaching opportunities, and measure call script compliance. Combining AWS services into SuccessKPI’s experience analytics platform was a no brainer. We are looking forward to bringing this valuable capability into the hands of large enterprises and government agencies.

Getting Started
A single API call is all it takes to extract rich insights from your customer conversations. You can start using Amazon Transcribe Call Analytics today in the following regions:

  • US West (Oregon), US East (N. Virginia),
  • Canada (Central),
  • Europe (London), Europe (Frankfurt),
  • Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Seoul), Asia Pacific (Tokyo), Asia Pacific (Sydney).

Please give this new feature a try in the AWS console, and let us know what you think. We always look forward to your feedback! You can send it through your usual AWS Support contacts or post it on the AWS Forum for Amazon Transcribe.

One last thing: if you’re looking for an easy to use omnichannel cloud contact center, you should definitely take a look at Amazon Connect and its ML powered analytics, Contact Lens.

– Julien

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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…

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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…

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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.

Prerequisites

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!

Cleanup

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.

Conclusion

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…

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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 BankingBot.zip 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 lambda_function.py 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.

Conclusion

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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