Deploying text-to-speech (TTS) on embedded systems presents challenges due to hardware limitations, real-time processing demands, and balancing quality with efficiency. Embedded devices often have constrained computational power, memory, and storage, making it difficult to run complex TTS models. Additionally, developers must optimize for latency, power consumption, and thermal constraints while maintaining acceptable speech quality. These trade-offs require careful design choices and technical compromises.
One major challenge is computational and memory limitations. Modern TTS systems, especially neural network-based models like Tacotron or WaveNet, require significant processing power and RAM. Embedded systems, such as microcontrollers or low-cost IoT devices, may lack the CPU/GPU capabilities to run these models in real time. For example, a Raspberry Pi might struggle with a high-latency TTS pipeline, causing delays in voice output. To address this, developers often use lighter architectures (e.g., FastSpeech2) or reduce model size via quantization and pruning. However, these optimizations can degrade audio quality or restrict voice naturalness, forcing trade-offs between performance and user experience.
Storage and power constraints further complicate deployment. High-quality TTS models require large voice datasets, which consume flash memory—a scarce resource in embedded systems. Storing multiple languages or voices might be impractical. For instance, a 50 MB model may exceed the storage of a device with 64 MB flash, necessitating aggressive compression or cloud offloading. Power consumption is also critical for battery-operated devices: continuous TTS processing can drain batteries quickly. Techniques like duty cycling (activating components only when needed) or using hardware accelerators (e.g., DSPs) help, but these add cost and design complexity. Balancing these factors is essential for creating viable embedded TTS solutions.