To combine text-to-speech (TTS) and speech recognition for full-duplex communication, the system must process incoming audio (speech recognition) and generate outgoing audio (TTS) simultaneously without blocking either process. This requires parallel processing, synchronization between components, and handling overlapping audio streams. The goal is to enable natural, real-time interactions where both parties can speak and listen at the same time, similar to human conversation.
First, the architecture needs separate threads or services for speech recognition and TTS. The speech recognition component continuously listens to the user’s audio input, converts it to text, and sends it for processing (e.g., to a chatbot or another user). Simultaneously, the TTS engine generates audio from the response text and plays it back. For example, in a voice assistant, the system might begin generating a response as soon as it detects a pause in the user’s speech, while still listening for interruptions. To avoid audio overlap issues, techniques like echo cancellation and voice activity detection (VAD) are used to suppress the system’s own TTS output from interfering with incoming speech recognition. APIs like WebRTC can manage bidirectional audio streams in web applications, while frameworks like PyAudio or sounddevice in Python handle low-level audio I/O.
Second, buffering and prioritization are critical. Speech recognition might produce partial results (e.g., interim transcripts) that the system can act on before the user finishes speaking. For instance, a live translation system could start translating and synthesizing speech in near-real time. However, TTS output must be timed carefully to avoid cutting off the user mid-sentence or creating confusing delays. Developers can use interruptibility flags to pause or adjust TTS playback when new speech is detected. Edge cases like overlapping audio require prioritization logic—for example, a customer service bot might lower its TTS volume when the user speaks, or delay its response until a natural break occurs. Latency optimization is key: both components must operate with minimal delay, often requiring hardware acceleration (e.g., GPUs for neural TTS models) and efficient network protocols (like gRPC for cloud-based services).
Finally, real-world use cases include voice-enabled collaboration tools (e.g., simultaneous interpretation in video calls) or interactive voice assistants that handle interruptions. For example, a telehealth app could allow a doctor and patient to speak naturally while the system transcribes and synthesizes responses in real time. Implementing this requires robust error handling—such as retrying failed ASR or TTS requests without blocking the pipeline—and testing under varying network conditions. Open-source tools like Mozilla DeepSpeech for speech recognition and Coqui TTS for synthesis can be integrated using message brokers (e.g., RabbitMQ) to decouple components. The end result is a system where both input and output audio streams coexist seamlessly, enabling fluid, human-like dialogue.