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Live transcriptions of F1 races using Amazon Transcribe

The Formula 1 (F1) live steaming service, F1 TV, has live automated closed captions in three different languages: English, Spanish, and French. For the 2021 season, FORMULA 1 has achieved another technological breakthrough, building a fully automated workflow to create closed captions in three languages and broadcasting to 85 territories using Amazon Transcribe. Amazon Transcribe…



The Formula 1 (F1) live steaming service, F1 TV, has live automated closed captions in three different languages: English, Spanish, and French.

For the 2021 season, FORMULA 1 has achieved another technological breakthrough, building a fully automated workflow to create closed captions in three languages and broadcasting to 85 territories using Amazon Transcribe. Amazon Transcribe is an automatic speech recognition (ASR) service that allows you to generate audio transcription.

In this post, we share how Formula 1 joined forces with the AWS Professional Services team to make it happen. We discuss how they used Amazon Transcribe and its custom vocabulary feature as well as custom-built postprocessing logic to improve their live transcription accuracy in three languages.

The challenge

For F1, everything is about extreme speed: with pit stops as short as 2 seconds, speeds of up to 375 KPH (233 MPH), and 5g forces on drivers under braking and through corners. In this fast-paced and dynamic environment, milliseconds dictate the difference between pole position or second on the grid. The role of the race commentators is to weave the multitude of parallel events and information into a single exciting narrative. This form of commentary greatly increases the engagement and excitement of viewers.

F1 has a strong affinity to cutting edge technology, and partnered with AWS to build a scalable and sustainable closed caption solution for F1 TV, their Over-the-top (OTT) platform, that can support a growing calendar and language portfolio. F1 now provides real-time live captions in three languages across four series: F1 in British English, US Spanish and French; and F2, F3, and Porsche Supercup in British English and US Spanish. This was achieved using Amazon Transcribe to automatically convert the commentary into subtitles.

This task provides many unique challenges. With the excitement of an F1 race, it’s common to have commentators with differing accents move quickly from one topic to another as the race unfolds. Being a sport steeped in technology, commentators often refer to F1 domain-specific terminology such as DRS (Drag Reduction System), aerodynamic, downforce, or halo (a safety device) for example. Moreover, F1 is a global sport, traveling across the world and drawing drivers from many different countries. Looking only at the 2021 season, 16/20 drivers had non-English names and 17/20 had non-Spanish names or non-French names. With the advanced customization features available in Amazon Transcribe, we tailored the underlying language models to recognize domain-specific terms that are rare in general language use, which boosted transcription accuracy.

In the following sections, we take a deep dive into how AWS Professional Services partnered with F1 to build a robust, state-of-the-art, real-time race commentary captioning system by enhancing Amazon Transcribe to understand the particularities of the F1 world. You will learn how to utilize Amazon Transcribe in real-time broadcasts and supercharge live captioning for your use case with custom vocabularies, postprocessing steps, and a human-in-the-loop validation layer.

Solution overview

The solution works as a proxy to Amazon Transcribe. Custom vocabularies are passed as parameters to Amazon Transcribe, and the resulting captions are postprocessed. The postprocessed text is then moderated by an F1 moderator before being transformed to captions that are displayed to the viewers. The following diagram shows the sequential process.

Live transcriptions: Understanding use case specific terminology and context

The output of Automatic Speech Recognition (ASR) systems is highly context-dependent. ASR language models benefit from utilizing the words across a fully spoken sentence. For example, in the following sentence, the system uses the words ‘WORLD CHAMPIONSHIP’ towards the end of the sentence to inform context and allow ‘FORMER ONE’ to be correctly transcribed as ‘FORMULA 1’.




Amazon Transcribe supports both batch and streaming transcription models. In batch transcription, the model issues a transcription using the full context provided in the audio segment. Amazon Transcribe streaming transcription enables you to send an audio stream and receive a transcription stream in real time. Generating subtitles for a live broadcast requires a streaming model because transcriptions should appear on screen shortly after the commentary is spoken. This real-time need presents unique challenges compared to batch transcriptions and often affects the quality of the results because the language model has limited knowledge of the future context.

Amazon Transcribe is pre-trained to capture a wide range of use cases. However, F1 domain-specific terminology, names, and locations aren’t present in the Amazon Transcribe general language model. Getting those words correct is nevertheless crucial for the understanding of the narrative, such as who is leading the race, circuit corners, and technical details.

Amazon Transcribe allows you to develop with custom vocabularies and custom language models to improve transcription accuracy. You can use them separately for streaming transcriptions or together for batch transcriptions.

Custom vocabularies consist of a list of specific words that you want Amazon Transcribe to recognize in the audio input. These are generally domain-specific words and phrases, such as proper nouns. You can inform Amazon Transcribe how to pronounce these terms with information such as SoundsLike (in regular orthography) or the IPA (International Phonetic Alphabet) description of the term. Custom vocabularies are available for all languages supported by Amazon Transcribe. Custom vocabularies improve the ability of Amazon Transcribe to recognize terms without using the context in which they’re spoken.

The following table shows some examples of a custom vocabulary.

Phrase DisplayAs SoundsLike IPA
Charles-Leclerc Charles Leclerc ʃ ɑ ɹ l l ə k l ɛ ɹ
Charles-Leclerc Charles Leclerc shal-luh-klurk
Lewis-Hamilton Lewis Hamilton loo-is-ha-muhl-tn
Lewis-Hamilton Lewis Hamilton loo-uhs-ha-muhl-tn
Ferrari Ferrari f ɝ ɹ ɑ ɹ ɪ
Ferrari Ferrari fuh-rehr-ee
Mercedes Mercedes mer-sey-deez
Mercedes Mercedes m ɛ ɹ s eɪ d i z

The custom vocabulary includes the following details:

  • Phrase – The term that should be recognized.
  • DisplayAs – How the word or phrase looks when it’s output. If not declared, the output would be the phrase.
  • SoundsLike – The term broken into small pieces with the respective pronunciations in the specified language using standard orthography.
  • IPA – The International Phonetic Alphabet representation for the term.

Custom language models are valuable when there are larger corpuses of text data that can be used to train models. With the additional data, the models learn to predict the probabilities of sequences of words in the domain-specific context. For this project, F1 chose to use custom vocabulary given the unique words and phrases that are unique to F1 racing.

Postprocessing: the final layer of performance boosting

Due to the fast-paced nature of F1 commentary with rapidly changing context as well as commentator accents, inaccurate transcriptions may still occur. However, recurring mistakes can be easily fixed using text replacement. For example, “Kvyat and Albon” may be misunderstood as “create an album” by the British English language model. Because “create an album” is an unlikely term to occur in F1 commentaries, we can safely replace them with their assumed real meanings in a postprocessing routine. On top of that, postprocessing terms can be defined as general, or based on location and race series filters. Such selection allows for more specific term replacement, reducing the chance of erroneous replacements with this approach.

For this project, we gathered thousands of replacements for each language using hours of real-life F1 audio commentary that was analyzed by F1 domain specialists. On top of that, during every live event, F1 runs a transcribed commentary through a human-in-the-loop tool (described in the next section), which allows sentence rejection before the subtitles appear on screen. This data is used later to continuously improve the custom vocabulary and postprocessing rules. The following table shows examples of postprocessing rules for English captions. The location filter is a replacement filter based on race location, and the race series filter is based on the race series.

Original Term Replacement Location Filter Race Series Filter

Another important function of postprocessing is the standardization and formatting of numbers. When generating transcriptions for live broadcasts such as television, it’s a best practice to use digits when displaying numbers because they’re faster to read and occupy less space on screen. In English, Amazon Transcribe automatically displays numbers bigger than 10 as digits, and numbers between 0–10 are converted to digits under specific conditions, such as when there are more than one in a row. For example, “three four five” converts to 345. In an effort to standardize number transcriptions, we digitize all numbers.

As of August 8, 2021, transcriptions only output numbers as digits instead of words for a defined list of languages in both batch and streaming (for more information, see Transcribing numbers and punctuation). Notably, this list doesn’t include Spanish (es-US and es-ES) or French (fr-FR and fr-CA). With the postprocessing routine, numbers were also formatted to handle integers, decimals, and ordinals, as well F1-specific lap time formatting.

The following shows an example of number postprocessing for different languages that were built for F1.

Human in the loop: Continuous improvement and adaptation

Amazon Transcribe custom vocabularies and postprocessing boost the service’s real-time performance significantly. However, the fast-paced and quickly changing environment remains a challenge for automated transcriptions. It’s better for a person reliant on closed captions to miss out on a phase of commentary, rather than see an incorrect transcription that may be misleading. To this end, F1 employs a human in the loop as a final validation, where a moderator has a number of seconds to verify if a word or an entire sentence should be removed before it’s included in the video stream. Any removed sentences are then used to improve the custom vocabularies and postprocessing step for the next races.


Minor grammatical errors don’t greatly decrease the understandability of a sentence. However, using the wrong F1 terminology breaks a sentence. Usually ASR systems are evaluated on word error rate (WER), which quantifies how many insertions, deletions, and substitutions are required to change the predicted sentence to the correct one.

Although WER is important, F1-specific terms are even more crucial. For this, we created an accuracy score that measures the accuracy of people names (such as Charles Leclerc), teams (McLaren), locations (Hungaroring), and other F1 terms (DRS) transcribed in a commentary. These scores allow us to evaluate how understandable the transcriptions are to F1 fans and, combined with WER, allow us to maintain high-quality transcriptions and improvements in Amazon Transcribe.


The F1 TV enhanced live transcriptions system was released on March 26, 2021, during the Formula 1 Gulf Air Bahrain Grand Prix. By the first race, the solution had already achieved a strong reduction in WER and F1-specific accuracy improvements for all three languages, compared to the Amazon Transcribe standard model. In the following tables, we highlight the WER and F1 specific accuracy improvements for the different languages. The numbers compare the developed solution using Amazon Transcribe using custom vocabularies and postprocessing with Amazon Transcribe generic model. The lower the WER, the better.

  Standard Amazon Transcribe WER Amazon Transcribe with CV and Postprocessing WER WER Improvement
English 18.95% 11.37% 39.99%
Spanish 25.95% 16.21% 37.16%
French 37.40% 16.80% 55.08%
Accuracy Group Standard Amazon Transcribe Accuracy Amazon Transcribe with CV and Postprocessing Accuracy Accuracy Improvement
English People Names 40.17% 92.25% 129.68%
Teams 56.33% 95.28% 69.15%
Locations 61.82% 94.33% 52.59%
Other F1 terms 81.47% 90.89% 11.55%
Spanish People Names 45.31% 95.43% 110.62%
Teams 39.40% 95.46% 142.28%
Locations 58.32% 87.58% 50.17%
Other F1 terms 63.87% 85.25% 33.47%
French People Names 39.12% 92.38% 136.15%
Teams 33.20% 90.84% 173.61%
Locations 55.34% 89.33% 61.42%
Other F1 terms 61.15% 86.77% 41.90%

Although the approach significantly improves the WER measures, its main influence is seen on F1 names, teams, and locations. Because the F1 specific terms are often in local languages, custom vocabularies, and postprocessing steps can quickly teach Amazon Transcribe to consider those terms and correctly transcribe them. The postprocessing step then further adapts the outcome transcriptions to F1’s domain to provide highly accurate automated transcriptions. In the following examples, we present phrases in English, Spanish, and French where Amazon Transcribe custom vocabularies, postprocessing, and number handling techniques successfully improved the transcription accuracy.


The custom vocabulary and postprocessing combination converted “LORIS JAMIL TODOS” to “LEWIS HAMILTON,” and the number handling routine converted the lap time to digits and added the appropriate punctuation (1:29.234).

For English, compare the original output “THE GERMAN DRIVER THE BASTION BETTER COMPLETED THE LAST LAP IN ONE 15 632” to the final transcription “THE GERMAN DRIVER SEBASTIAN VETTEL COMPLETED THE LAST LAP IN 1:15.632.”

The custom vocabulary and postprocessing combination converted “THE BASTION BETTER” to “SEBASTIAN VETTEL.”


The custom vocabulary and postprocessing combination converted “LES MISS MILLE TONNE” to “LEWIS HAMILTON,” and the number handling routine converted the numbers to digits.

The following short video shows live captions in action during the Formula 1 Gulf Air Bahrain Grand Prix 2021.


In this post, we explained how F1 is now able to provide live closed captions on their OTT (Over-The-Top) platform to benefit viewers with accessibility needs and those who want to ensure they do not miss any live commentary.

In collaboration with AWS Professional Services, F1 has set up live transcriptions in English, Spanish, and French by using Amazon Transcribe and applying enhancements to capture domain-specific terminology.

Whether for sport broadcasting, streaming educational content, or conferences and webinars, AWS Professional Services is ready to help your team develop a real-time captioning system that is accurate and customizable by making full use of your domain-specific knowledge and the advanced features of Amazon Transcribe. For more information, see AWS Professional Services or reach out through your account manager to get in touch.

About the Authors

Beibit Baktygaliyev is a Senior Data Scientist with AWS Professional Services. As a technical lead, he helps customers to attain their business goals through innovative technology. In his spare time, Beibit enjoys sports and spending time with his family and friends.

Maira Ladeira Tanke is a Data Scientist at AWS Professional Services. She works with customers across industries to help them achieve business outcomes with AI and ML technologies. In her spare time, Maira likes to play with her cat Smila. She also loves to travel and spend time with her family and friends.

Sara Kazdagli is a Professional Services consultant specialized in Data Analytics and Machine Learning. She helps customers across different industries to build innovative solutions and make data-driven decisions. Sara holds a MSc in Software Engineering and a MSc in Data Science. In her spare time, she like to go on hikes and walks with her Australian shepherd dog Kiba.

Pablo Hermoso Moreno is a Data Scientist in the AWS Professional Services Team. He works with clients across industries using Machine Learning to tell stories with data and reach more informed engineering decisions faster. Pablo’s background is in Aerospace Engineering and having worked in the motorsport industry he has an interest in bridging physics and domain expertise with ML. In his spare time, he enjoys rowing and playing guitar.


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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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