Looking Enhance Address Transcription Accuracy for...
# support
I am seeking assistance to integrate a solution that utilizes a knowledge base of street addresses and street names. This knowledge base will support the transcription process for our taxi dispatch system, which operates over the phone. The goal is to accurately capture street addresses communicated by users, despite potential pronunciation challenges. It's important that the system references this knowledge base during, not after, transcription to ensure accuracy. Can anyone help develop this capability for our system?
Hi @Chaim T, I'm an AI assistant for Vapi. I'll analyze available sources and get back to you shortly.
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### Answer Integrating a knowledge base to enhance address transcription accuracy, especially for applications like a taxi dispatch system, involves a few steps. While Vapi provides a robust framework for building voice AI applications, including handling the transcription of speech to text via its transcriber configuration, the specific scenario of utilizing an external knowledge base for real-time transcription refinement is not directly covered in the provided documentation. However, you can leverage Vapi's flexibility to integrate with external services or databases (such as a knowledge base of street addresses) indirectly. Here's a high-level approach to achieve your goal: 1. **Transcription**: Utilize Vapi's transcription capabilities to convert speech to text. This can be set up using the transcription settings within your assistant's configuration where you specify the transcriber provider and model. You might consider using a transcriber that's optimized for your use case, such as
for better accuracy [(1)]() [(2)]() . 2. **Knowledge Base Query**: After initial transcription, you can use a custom function within your system to query your knowledge base of street addresses and names. This function can be designed to identify and correct any discrepancies based on the context of the conversation and known common errors or pronunciation challenges. 3. **Real-time Correction**: Implement a mechanism to feed the corrected addresses back into the conversation flow. This might involve an intermediary layer in your application logic that handles both the output from Vapi and the input from your knowledge base correction function. 4. **Continuous Learning**: Optionally, incorporate a feedback loop where
the system learns from its corrections over time, further refining its accuracy. This could involve machine learning techniques or simpler heuristic-based adjustments. For steps 2 to 4, while Vapi provides a powerful platform for creating and managing voice assistants with transcription capabilities, integrating a real-time correction mechanism with a separate knowledge base would require custom development outside of Vapi's standard offerings. This would involve backend development work on your part to manage the interaction between Vapi's transcription output and your knowledge base. While the documentation does not provide a direct example of integrating with a knowledge base for real-time transcription correction, Vapi's API and flexible platform design allow for such custom integrations. You would utilize the API to manage assistants, transcriptions, and other functionalities, integrating them with your own systems to achieve the desired address transcription accuracy [(1)]() [(2)]() [(3)]() . For detailed guidance on configuring transcription settings or managing assistants via the API, refer to the official Vapi documentation on creating and updating assistants [(2)]() [(3)]() .
Got it. Tagging @User for additional help.
@Henryk can you help me with this?
Probably could, but would have wait until Tuesday night 🙂 In the meantime could just message me @Chaim T and we could discuss some ideas but won't actually build anything until then.