Machine learning is a subset of artificial intelligence that trains a machine how to learn. It is the proposition that systems can learn from data, identify patterns, and come to a decision. Machine learning is gaining such momentum that the total funding allocated to ML worldwide during the first quarter of 2019 was $28.5 billion. Moreover, it’s been found that 49% of companies are exploring to use ML.
Built a sentiment analysis solution that has expanded our customer sentiment visibility from 12% to 100%.
Transcribe automatically converts speech to text. That text can then be examined using Contact Lens for Amazon Connect to better understand the reasons clients are calling and any snags in the Deliveroo care experience, permitting the company to enhance its practices.
Transcribe permits Bongo’s Auto Analysis™ to quickly and accurately transcribe learner videos and provide feedback on the video that helps a learner employ a ‘practice, reflect, improve’ loop. This allows learners to improve content comprehension, retention, and learning outcomes and reduces instructor assessment time since they are viewing a more valuable work product. Teachers can focus on delivering insightful feedback without spending time on the metrics the Auto Analysis™ produces automatically.
Slack Huddles connect users to each other and utilize Amazon Transcribe and Chime SDK to offer live meeting subtitles, so no details are lost. Users can also send Slack Clips, short audio and video messages which are transcribed into searchable text by Amazon Transcribe.
BYJU automates manual tasks using Amazon Transcribe, which makes it straightforward for developers to add speech-to-text capabilities to their applications. BYJU’S uses Amazon Transcribe for its discussions and to record its customer service calls. Now, the company is capable to transcribe a call within a few minutes. Moreover, it can use the transcripts to examine customer feedback and enhance applications.
AWS is able to assist us in concentrating on the things that really count: our core mission, our core product, and our core competency.
Transcription is step one for anticipating the needs of clients who call us, and we build on top of it. Because AWS services do their job so well out of the box, we have the flexibility to be innovative and build things on top of them.
When evaluating a speech-to-text service, we focused on finding a service that could create a safe community for our users by automatically redacting banned words. These banned words are typically slang and not identified by standard speech-to-text services. With Amazon Transcribe and its custom vocabulary feature, banned terminology can be recognized and redacted.
Amazon Transcribe is a powerful tool; it performs transcription with extremely high accuracy, which grows every day. F1’s use-case was quite challenging; the combination of incredibly high speed and dynamic commentary from multiple contributors, a global vocabulary and niche technical terminology. Working in close collaboration with AWS, we created and trained a scalable subtitling solution with precision and performance that matches human Closed Captioners.
With more than 275 million minutes of customer interactions each year, Intuit uses Contact Lens for Amazon Connect, which delivers contact center analytics powered by machine learning (ML), to deliver accurate call transcriptions, redaction of sensitive data, and automated call metrics to define the effectiveness of its contact center. Contact Lens builds on AWS machine learning services, using Amazon Transcribe, a service that automatically transforms speech to text, to generate call transcripts and Amazon Comprehend, a natural language processing (NLP) service, to extract sentiment.
Epiq not only built a cost-effective solution that enabled it to automate, accelerate, and improve the accuracy of legal transcription, but it also revolutionized the way transcription is achieved in the industry. In a few weeks, Epiq delivered an AWS-powered transcription solution that is 5% more accurate than other third-party transcription engines and reduces costs by a factor of 15x.
In our mission to deliver customers with fair and transparent pricing along with excellent service, we examine millions of minutes of contact center calls each month. We’ve leveraged our own domain-exact data to train custom speech recognition models using Amazon Transcribe. These custom models supply enhanced transcription accuracy and allow us to more efficiently and intelligently identify, understand, and serve our client’s energy needs.
Every day we analyze 225,000 minutes of live talk radio to create thousands of short, topical segments of information for playlists and searches. We chose Amazon Transcribe because it is a remarkable speech recognition engine that enables us to transcribe live audio content for our downstream content production work streams. Transcribe delivers a robust system that can simultaneously convert a hundred audio streams into text for a reasonable cost. With this high-quality output text, we are then able to quickly process live talk radio episodes into consumable parts that provide next-gen listening experiences and drive higher engagement.
GE Appliances procedures millions of minutes of customer calls a month. Using Amazon Connect, Amazon Lex, and Amazon Polly, we can automate easy tasks such as looking up product information, taking down consumer details, and answering common questions before an agent answers. This in turn allows us give back time, the most precious commodity, to our consumers. We added Amazon Transcribe to create call transcripts for automatic analysis to continuously improve the process.
At Northwestern Mutual, we wanted to decrease the amount of time our financial representatives spent logging compliant case notes for each of their clients. We originally created an app with a voice-to-text feature that used a phone’s native capabilities, but only 12 percent of financial agents used it because it took longer to fix the transcribed notes than to type it themselves. To best solve the low adoption situation and provide far greater accuracy, we needed to think of a new approach. Amazon Transcribe stood out as the most exact platform with the fewest transcription problems– for our use case, we estimated the accuracy to be around 95 percent, whereas other services we looked at were in the 70-percent range.
Machine learning in business has traditionally been the domain of experts, requiring coding skills and extensive knowledge of AI algorithms. As the best machine learning development company offers extensive services to help startups and small businesses make the most of advanced technologies to expand their operations and capture new markets.