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Boost transcription accuracy of class lectures with custom language models for Amazon Transcribe

Many universities like transcribing their recorded class lectures and later creating captions out of these transcriptions. Amazon Transcribe is a fully-managed automatic speech recognition service (ASR) that makes it easy to add speech-to-text capabilities to voice-enabled applications. Transcribe assists in increasing accessibility and improving content engagement and learning outcomes by connecting with both auditory and…

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[]Many universities like transcribing their recorded class lectures and later creating captions out of these transcriptions. Amazon Transcribe is a fully-managed automatic speech recognition service (ASR) that makes it easy to add speech-to-text capabilities to voice-enabled applications. Transcribe assists in increasing accessibility and improving content engagement and learning outcomes by connecting with both auditory and visual learners.

[]When transcribing content that is more specialized or domain-specific such as biology, Amazon Transcribe offers custom language models (CLM). One common problem we see is the difficulty in accurately transcribing certain subjects. In this post, we show how you can harness readily available content to train a CLM in Amazon Transcribe and boost the transcription accuracy on scientific subjects like biology. This feature allows you to submit a corpus of text data to train custom language models that target domain-specific use cases. Using CLM is easy because it capitalizes on existing data that you already possess (such as website content, curriculum, and lesson plans). Since this is “custom”, you can easily use the approach presented here and create a CLM for your subject of interest.

[]This blog’s main purpose is to show how data can be easily downloaded from Wikipedia to generate a training corpus for CLM.

[]In this blog, we will refer to a few publicly available biology audio lectures from MIT. Amazon Transcribe might recognize the following advanced scientific terms:

[]“Prokaryotic cells” as “Pro carry ah tick cells

[]“Endoplasmic reticulum” as “Endo Plas Mick Ridiculous um

[]“Vacuoles” as “Vac u ALS

[]“Flagella” as “Flu Gela

[]These results shouldn’t be interpreted as a full representation of the Amazon Transcribe service performance—it’s just one instance for a very specific example.

Solution overview

[]With the CLM feature in Amazon Transcribe, you can build your own custom model for your class course content and improve the transcription accuracy of your class lectures.

[]The CLM feature in Transcribe carries three stages for building a custom model:

  1. Prepare training data
  2. Train a CLM model
  3. Transcribe an audio file using the CLM model and evaluate the results

Prepare training data

[]The Amazon Transcribe CLM feature requires training data that is specific to that particular domain. In our example, we require training data specific to biology. We can obtain training data from various sources. In our case we obtained it from Wikipedia using the following code. We can further improve the CLM’s accuracy using ground truth transcripts as tuning data. For more information, see Improving domain-specific transcription accuracy with custom language models.

[]Written in Python, this code pulls various biology-related articles from Wikipedia, and requires you to provide a few key terms related to the domain of interest. It then fetches Wikipedia articles on those key title terms if they exist, and ignores those articles if the terms don’t exist. Then our training data is ready. In this example, the code upon completion creates 137 separate text files. You can upload these text files to a folder in an Amazon Simple Storage Service (Amazon S3) bucket.

!pip3 install beautifulsoup4 !pip3 install nltk import nltk nltk.download(‘punkt’) from nltk import tokenize import re import urllib.request from bs4 import BeautifulSoup # Create a list of key terms related to biology keywords_list = [“Abdominal cavity”, “Absorption”, “Acclimation”, “Achondroplasia”, “Acid”, “Behaviour”, “ACTH”, “Adrenocorticotropic”, “Hormone”, “Aerobic”, “Amoeba”, “Amoeboid”, “Anabolism”, “Anabolic”, “Anaerobic”, “Anagen”, “Anastomosis”, “Anatomy”, “Anterior”, “Articulate”, “Blastodisc”, “Blastoderm”, “Binocular”, “Bolus”, “Boli”, “Catabolism”, “Catabolic”, “Caudal”, “Choana”, “Coelom”, “Columnar”, “Epithelium”, “Conical”, “Corium”, “Cranial”, “Dimorphism”, “Distal”, “Dorsal”, “Ectoderm”, “Electrolyte”, “Endocardium”, “Endoderm”, “Entoderm”, “Gamete”, “Germ”, “Gonads”, “Gonadotropins”, “Heterophile”, “Homeothermic”, “Hyperthermia”, “Hypothermia”, “Ingest”, “Infection”, “Infestation”, “Lateral”, “Longitudinal”, “Lunar”, “Median”, “Meiosis”, “Chromosome”, “Metabolism”, “Living organism”, “Mitosis”, “Mesoderm”, “Myocardium”, “Neo”, “Ovum”, “Paleo”, “Respiration”, “Papilla”, “Papillae”, “Exocrine gland”, “Peri”, “Pericardial”, “Heart”, “Peritoneal”, “Intestine”, “Abdomen”, “PH”, “Phagocyte”, “White blood cell”, “Foreign body”, “Bacteria”, “Physiology”, “Organism”, “Plantar”, “Pleural”, “Lung”, “Poikilothermic”, “Animal”, “Body”, “Polymorphonuclear”, “Nucleus”, “Posterior”, “Proximal”, “Pulmonary”, “Veins”, “Purkinje fibres”, “Muscle”, “Fibres”, “Sagittal”, “Tissue”, “Sebaceous”, “Serous”, “Membrane”, “Squamous”, “Syncytium”, “Protoplasm”, “Telogen”, “Thoracic”, “Cavity”, “Body cavity”, “Diaphragm”, “Transverse”, “Ventral”, “Virulent”, “Disease”, “Biology”, “Human cell”, “Animal cell”, “Cell structure”, “Zoology”, “DNA”, “Plant cell”, “Biophysics”, “Cell and molecular biology”, “Computational biology”, “Ecology”, “Evolution”, “Environmental biology”, “Forensic biology”, “Genetics”, “Marine biology”, “Microbiology”, “Biosciences”, “Natural science”, “Neurobiology”] # Purge any duplicates from list keywords_list = list(set(keywords_list)) print(“Size of keyword list =”, len(keywords_list)) # Write output to a folder def output_to_file(data, keyword): file_location = “./”+keyword+”.txt” with open(file_location, “w”, encoding=”utf-8″) as f: f.write(data) f.close() # Helper method to get html text from wikipedia def extract_html(keyword): try: fp = urllib.request.urlopen(“https://en.wikipedia.org/wiki/”+keyword) html = fp.read().decode(“utf8”) fp.close() return html except: print(“Page for “+keyword+” does not exist”) return None # Helper method to extract data from html text def get_data(html): extracted_data = [] soup = BeautifulSoup(html, ‘html.parser’) for data in soup.find_all(‘p’): res = tokenize.sent_tokenize(data.text) for txt in res: txt2 = re.sub(“[([].*?[)]]”, “”, txt) txt2 = txt2.strip() if len(txt2)>0: extracted_data.append(txt2) return extracted_data # Download data from wikipedia to local text files count = 0 for keyword in keywords_list: keyword = keyword.replace(” “,”_”) html = extract_html(keyword) if html: count += 1 data = “n”.join(get_data(html)) output_to_file(data,keyword) print(“Was able to download text for “+ str(count) + ” out of “+str(len(keywords_list))+” keywords”)

Train a Custom Language Model

[]We use this training data to train our CLM in Amazon Transcribe. To do so, we can use the AWS Management Console, the AWS Command Line Interface (AWS CLI), or the AWS SDK. The methodology shown in this post uses the console.

  1. On the Amazon Transcribe console, choose Custom language model in the navigation pane.

[]

  1. Choose Train model.
  2. For Name, enter a name for your model.
  3. For Language, choose the language of your model (for this post, we choose English, US).
  4. For Base model, if your audio files have a sample rate greater than 16 kHz, select Wide band.
  5. For Training data, enter the S3 folder path for your training data.
  6. Create an AWS Identity and Access Management (IAM) role if you don’t have an existing role with the required permissions.
  7. Choose Train model.

[]

[]Your model should be ready after a few hours. Make sure that your training data is in UTF-8 format. For more information, see Improving domain-specific transcription accuracy with custom language models.

[]When your model is ready, you can use it to create transcriptions.

Transcribe and evaluate the results

[]In this section, we compare the transcription output from standard Amazon Transcribe with the CLM output.

[]We took the standard biology audio file as input to show how CLM improves the results. The words highlighted in red show errors in transcription, and the ones highlighted in green show how those errors are fixed by the CLM.

Snippet 1 – Ground Truth
Outside the nucleus, the ribosomes and the rest of the organelles float around in cytoplasm, which is the jelly like substance. Ribosomes may wander freely within the cytoplasm or attach to the endoplasmic reticulum sometimes abbreviated as ER.
Snippet 1 – Standard Amazon Transcribe Snippet 1 – Amazon Transcribe with CLM
Outside the nucleus, the ribosomes and the rest of the organelles float around in cytoplasm, which is the jelly like substance. Ribosomes may wander freely within the cytoplasm or attach to the end a plasma critical, Um, sometimes abbreviated as E. R. Outside the nucleus, the ribosomes and the rest of the organelles float around in cytoplasm, which is the jelly like substance. Ribosomes may wander freely within the cytoplasm or attach to the endoplasmic reticulum, sometimes abbreviated as E. R.
Snippet 2 – Ground Truth
Another unique feature in some cells is flagella. Some bacteria have flagella. A flagellum is like a little tail that can help a cell move or propel itself.
Snippet 1 – Standard Amazon Transcribe Snippet 1 – Amazon Transcribe with CLM
Another unique feature in some cells is flat Gela. Some bacteria have fled. Gela, a flagellum, is like a little tail that can help us sell, move or propel itself. Another unique feature in some cells is flagella. Some bacteria have flagella. A flagellum is like a little tail that can help a cell move or propel itself.

[]To demonstrate this further, we downloaded several publicly available biology audio lectures from MIT, namely lectures 1, 3, and 4. Results from this exercise are reported in the following table using word error rate (WER) as a metric. WER is a standard metric used to measure transcription accuracy, where accuracy = (1.0 – WER). In this test, we used the asr-evaluation Python module for WER calculations.

Standard Amazon Transcribe WER Amazon Transcribe CLM WER Standard Amazon Transcribe Accuracy Amazon Transcribe CLM Accuracy # Words Words Improved by CLM
Lecture 1 9.5% 7.4% 90.5% 92.6% 5,678 119
Lecture 3 13.2% 11.6% 86.8% 88.4% 7,578 121
Lecture 4 12.2% 10.4% 87.8% 89.6% 7,534 135

[]As is evident from the results, transcription accuracy improved through the use of CLM. The following are some of the transcription errors that the CLM fixed:

[]“file a Chinese” corrected to “Phylogenies”

[]“Metas Oona” corrected to “Metazoa”

[]“File Um” corrected to “Phylum”

[]“Endo plans particular” corrected to “Endoplasmic reticulum”

[]A lower WER is better. These WERs aren’t representative of overall Amazon Transcribe performance. All numbers are relative to demonstrate the point of using custom models over generic models, and are specific only to this singular audio sample. The number of words accurately transcribed by CLM is pretty significant! As you can see, although Amazon Transcribe’s generic engine performed decently in transcribing the sample audio from the biology domain, the CLM we built using training data performed even better! These comparative results are unsurprising because the more relevant training and tuning that a model experiences, the more tailored it is to the specific domain and use case.

Conclusion

[]In this post, we showed how results from the custom language feature of Amazon Transcribe can improve transcription accuracy on difficult specialized audio topics, such as biology lectures. Further improvements are possible by using course materials such as textbooks and relevant articles as additional training data. You can use some of the ground truth audio transcripts as tuning data.

[]You can also use the custom vocabulary feature in Amazon Transcribe in conjunction with CLM to provide pronunciations hints for particularly troublesome words. For more information, see Custom vocabularies.

[]As you start building a CLM for your use case, make sure that you train it on appropriate data for that particular subject. You can use the code provided in this post to source domain-specific tuning or training data from public websites such as Wikipedia. Try it out yourself and let us know how you do in the comments!

About the Author

[]Raju Penmatcha is a Senior AI/ML Specialist Solutions Architect at AWS. He works with education, government, and nonprofit customers on machine learning and artificial intelligence-related projects, helping them build solutions using AWS. Outside of work, he likes watching movies and exploring new places.



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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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