The transcribers struggle to cope with non western names, has anyone found work arounds for this.

@Dan I understand that transcribing non-Western names can be challenging for some voice recognition systems. Here are a few workarounds that might help improve the accuracy: 1. **Training the Model**: If the transcriber allows for personalized voice training, you could try training the model with the specific non-Western names that you need it to recognize. 2. **Phonetic Spelling**: Use a phonetic spelling for the names when possible. This can sometimes help the transcriber better understand and accurately transcribe the names. 3. **Post-Processing**: Implement a post-processing step where you manually correct the names using a predefined list of non-Western names that the transcriber commonly misinterprets. 4. **Provider Selection**: Check if there are other providers that support a wider range of languages and dialects, which might handle non-Western names better. You can refer to the transcribers tab on your dashboard for more information on the languages supported by each provider. 5. **Custom Vocabulary**: Some transcribers allow you to add a custom vocabulary or a list of names that it should recognize. Adding non-Western names to this list can improve accuracy. 6. **Contextual Clues**: Providing the transcriber with more context can sometimes help it infer the correct names based on the surrounding words. If you need assistance with any specific feature or setting, please let me know, and I'll be happy to guide you further.

can you share call id, and decribe expected vs actual responses with translations.