Use Cases

Go Beyond Speech Recognition with Symbl APIs

Generating a transcription with speech-to-text APIs isn’t enough to get true value out of every conversation. Symbl’s Conversation API gives developers everything they need to deploy amazing experiences.

The current challenges of transcription only

Audio transcriptions may help companies document conversations, but transcription alone is not very useful. Speech-to-text APIs don’t provide additional insight or important contextual details. The analysis of conversations is what provides the most value to any business. To conduct that analysis, you need more information, beyond the words spoken. Uncovering that information is a difficult task, and often, development teams work to find the solution.

Should you build or buy a solution to improve transcriptions?

As companies expand into different initiatives, the same question comes up again and again. Should you build or buy a solution to support business goals? When thinking about AI and transcription, there are also additional considerations that must factor into the decision to build or buy.

Disadvantages of relying on a rule-based system

Rules-based systems take a lot of time and manual work to be successful. Beyond that, the system will only learn according to pre-defined rules, which limits the coverage of depth you can achieve. The capacity of the system is likely to be lower than you expect.

Disadvantages of relying on deep learning

Deep learning systems depend entirely on the data you provide. You need to have a high volume of quality data, and again, the process of training is time-consuming and costly. If you ever need to change your requirements, you’ll need to start over with new sets of data. This isn’t the best use of development time and resources.

Any build will also likely be missing a critical component of the conversation: context. This is what makes choosing a solution built to provide that context the best option for most businesses. You can add capabilities without spending time training models.

Using to extract intelligence from transcriptions

The Platform APIs offer a plug-and-play solution for developers to add conversational intelligence both in real time and asynchronously to their communication products and workflows. Here’s what provides to help you go beyond basic speech recognition, and extract contextual understanding from conversations to create more robust functionality in your applications.

1. Sentiments of the conversation

Discover the intensity of any sentiment in a conversation. Having this capability can help your organization drive meaningful change. For example, in sales, a team can gain an understanding of customers’ sentiments across conversations to inform next steps.

2. Sentiments on topics’s API for Topics with sentiment analyzes messages, returning their polarity with a suggestion. This sentiment analysis is powerful because it allows people to figure out what points resonated well during a conversation and what did not. This can inform additional steps to take after a call or it can serve as training to guide calls in the future.

3. Trackers

In human conversation, people may talk about the same intent but do not express themselves with the same exact phrases. For example, one person might say, “We now have sufficient funding for that project,” while another person might respond by saying, “So, the project budget looks good.” Both of these phrases express the same idea with different words, which is not a problem in terms of human-to-human understanding. But, that understanding can get lost in simple transcription. With Symbl’s Trackers API (currently in beta), customers can define and modify groups of names with key phrase and words to their choice and detect specific or “contextually similar” occurrences of it in any conversation in real time or asynchronously, which can lend clearer context to the transcription.

4. Entities and custom entities

Our API provides you with the functionality to extract entities (custom, location, person, date, number, organization, datetime, daterange, and more) from the conversation. This way, you can empower others to understand important people, places, or times that come up throughout a call or meeting.

5. Action items, ideas, questions, and follow-ups highlights all action items and follow-ups, ensuring that you catch every piece of information from a meeting that requires attention. This is a specific outcome that requires one or more people in the conversation to take an action, like setting up a meeting or completing a task. With, these next actions can be captured automatically so that everyone knows what they need to do after the meeting.  You can also use to track any questions that were asked during the conversation and any ideas discussed.

6. Topics of discussion

Conversations are unpredictable. They’re often all over the place and flow in and out of different points of discussion, making them difficult to analyze. Symbl’s topic model provides insights on the internal conversation structure of how concepts relate to each other in a discussion. This is different from traditional topic modeling algorithms that depend on frequency, probability, and distribution.

7. Parent-child hierarchy of topics (and sub-topics)

Any conversation may focus on a central theme, but every meeting also presents an opportunity to touch on new or unexpected points that expand knowledge or generate ideas. Our topic hierarchy understands customer conversation and returns the parent topic and its child topics. Here’s how we define parent topics and child topics:

Parent Topic: These are the key points that speakers of the meeting expand on and discuss at length. By identifying the main topics that come up during every conversation, people can more quickly and easily understand takeaways from the call.

Child topic: These are the subtopics that aggregate or originate from the parent topic itself. Child Topics link to the parent topic. They typically form the time chunks of the parent topics in a specific way and tie to themes throughout the meeting.

Leverage these features and get context faster

You can build a model to improve transcriptions, but this is no easy feat. Despite your best efforts, you may end up putting time in, only to end up with an error-prone system that’s inflexible and doesn’t provide the information required to drive business forward.

Buying the right solution can eliminate those issues.’s conversation intelligence API can help you take transcription to the next level by providing the context you’d otherwise miss. We provide tools that allow you to extract valuable insights from every conversation. Learn more about our APIs here.

Additional reading: