To combine text-to-speech (TTS) and speech recognition for full-duplex communication, both systems must operate simultaneously, allowing real-time interaction where speech synthesis and recognition happen without waiting for the other to finish. This requires parallel processing: TTS generates audio output while speech recognition processes incoming audio input. For example, a voice assistant could respond to a user’s query via TTS while still listening for interruptions or follow-up commands. To achieve this, developers need to manage audio streams independently, avoid feedback loops, and synchronize inputs and outputs to prevent overlap or latency issues. Tools like threading, asynchronous APIs, or dedicated audio buffers are essential to handle concurrency.
A practical implementation might involve separating the audio input (microphone) and output (speakers) pipelines. For instance, using a library like PyAudio in Python, developers can create separate threads for recording audio (for speech recognition) and playing synthesized speech (from TTS). Echo cancellation algorithms or noise suppression (e.g., WebRTC’s noise reduction) can mitigate interference between the system’s own TTS output and the user’s speech input. In a customer service chatbot, this setup would let the bot read product details aloud while simultaneously detecting when a user says “stop” to pause the explanation. Another example is a real-time translation app, where spoken input in one language is translated and spoken aloud in another language without manual turn-taking.
Developers should prioritize low-latency TTS and speech recognition models to minimize delays. For example, using a fast TTS engine like TensorFlowTTS or a cloud API like Google’s Text-to-Speech with streaming support ensures quick responses. Speech recognition systems like Whisper or Amazon Transcribe can process audio chunks incrementally to avoid waiting for full sentences. Code structure matters: a state machine can manage transitions between listening and speaking states, while a circular buffer can store overlapping audio data during mode switches. Testing with real-world scenarios—like overlapping speech or background noise—is critical to refine the system’s responsiveness and accuracy. By balancing parallelism, synchronization, and latency optimization, developers can create seamless full-duplex interactions.