Handling variations in accents and understanding specific keywords or phrases can be a challenging aspect of using text-to-speech (TTS) and automatic speech recognition (ASR) systems. While Google Cloud Text-to-Speech API is a powerful tool, it may not always perform perfectly in all situations. Here are some strategies you can consider to improve the accuracy of your voice recognition system:
Provide a phonetic transcription of hard-to-understand words or phrases. For example, you can specify how “Huevos Rancheros” is pronounced. Google Cloud Text-to-Speech API allows you to use SSML (Speech Synthesis Markup Language) to provide phonetic hints.
Train a custom language model for your specific use case. This can help improve recognition accuracy for domain-specific terms and phrases like “The CEO Burger.” You might need to use Google’s Speech Recognition service for this.
Continuously test and fine-tune your system based on real-world usage data. Collect and analyze user interactions to identify common misinterpretations and improve your recognition system over time.
Remember that perfect speech recognition is challenging, and even the most advanced systems can struggle with accents and uncommon phrases. It’s important to provide users with alternative means of interaction and continually refine your system to improve its accuracy.