Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately. Amazon Transcribe can be used to transcribe customer service calls, to automate closed captioning and subtitling, and to generate metadata for media assets to create a searchable archive. Amazon Transcribe Medical can be added to provide medical speech to text capabilities to clinical documentation applications.
$0
per second
Verint Speech and Text Analytics
Score 8.4 out of 10
N/A
Verint Speech Analytics is a tool used to transcribe and analyze millions of calls to discover customer insights and improve contact center performance in the cloud.
Amazon Transcribe can be an excellent tool for businesses where being able to convert speech or audio to text, in a searchable and reportable form, would be useful. For a call center (inbound or outbound), the ability to have a rich transcription of each call (and being able to search it for keywords) is an incredibly valuable benefit. For business meetings, being able to turn a 60 or 90-minute call into a readable transcript to search or refresh yourself or others is a very large time saver which will help you work more efficiently. The software does offer many deeper integrations, such as being able to track script usage (for call centers) or interruptions, deviations, etc.. which would be very valuable to a management team and for training purposes.
Using the verint speech to troubleshoot the mobile app and before we did not have any viability on this topic before. Also before we had no way to look at anything that the guest had going on and now we are really able to drill down in every way to take care of the guest needs
One of the things that I find extremely beneficial about Verint Speech Analytics is the transcription. Sometimes you have an hour call, an hour and a half call, and you don't have time to listen to an hour and a half call. The beauty is that we can find the transcription and then we can look in it. It specifies where if you look in the interaction details in the back of the call, you can actually see the transcription. You can see it'll identify where in the call, what line at what timestamp did that call hit your category and with what string, with what term did it hit. And that is extremely beneficial. And we're trying to go through and find those little points of, sometimes they're points of abrasion, sometimes there are things that are going really well. I love that we have a transcription. I love that we can also see the screens that is vital because a lot of times, and again, not that we're trying to find something bad, but the reality is in any industry, there's in any call center environment, should I say, there's call avoidance. There's always going to be some agents, it's just in any call center, in any industry, it happens.
Or sometimes our agents are just struggling. And if you see in real life, for example, a very high silence time, when we notice a trend, we'll start going into those calls and actually looking at those screens and seeing, is silence time call avoidance or could it be the agents are having difficulty utilizing the resources? Are the resources not clear? Is that what the delay is? Are they having trouble trying to find the answer? And a lot of times we found that too. We found they're searching and clicking and searching and clicking and it's because they just didn't know where to go. So that is a coaching opportunity. And sometimes it's a coaching opportunity, not so much in a positive way because they're purchasing things from Amazon. But I think, like I said, that's inherent in any call center environment. I do find the screens, the transcription, the trend analysis that's available within speech, there are so many things I could literally sit here and talk to you for quite some time.
We love it. And I'm even more excited personally now because I learned so much, as I was saying, about being in the cloud and being in that open-source environment where all of these enhancements that Verint has. And they're so often that they enhance their products and their offering now these little bots and AI to help with all of these different aspects, but that's only available if you're in the cloud and the open source. So there's a lot of benefit in being in prem, in net platform, which is where we are. And we were able to provide a lot of meaningful insight and guidance into a lot of different pain points in the company. But I anticipate, and I have full confidence, that we'd be a much more effective team once we're able to move into the cloud. So I'm very hopeful for that. I'm anticipating what else we could discover.
Automated scrubbing of PCI and PII data from transcription transcripts and recordings. Using AI to detect the payment screens in a real time manner vs having to program triggers.
Easier way to bulk export transcriptions and recordings for our legal teams.
This platform offers a wide range of features inclusive of the transcript generation which is text analysis and speech analysis. This helps in tracking the customer requirements and getting them a product they desire. Also the API integration with other tools have a scope of improvement. The UI can be improved and costing can be reduced
I use Google Cloud Speech to Text and Amazon Transcribe. What makes Amazon Transcribe better for me is the accuracy of the audio-to-text conversion. I have found out that Amazone Transcribe is better at handling homophones, contractions, abbreviations, and acronyms. Another feature that makes Amazon Transcribe my No. 1 choice is its use of punctuation marks. I can also feed my own list of vocabulary into Amazon Transcribe to help me acquire better results.
Verint Speech Analytics stacks up well against workforce management, we use workforce management mainly for forecasting and scheduling. We selected Speech Analytics because we want to know not only why customers are calling but what is being said in calls. Sometimes customer might get angry or they might ask for a supervisor so when we need to look at escalated calls Speech Analytics helps us pick those out.
The system is relatively reliable with minimal downtime. However, when there is an issue, it is often addressed slowly and causes significant business issues
