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Inspired by a human speech chain mechanism, a machine speech chain frameworkbased on deep learning was recently proposed for the semi-superviseddevelopment of automatic speech recognition (ASR) and text-to-speech synthesisTTS) systems. However, the mechanism to listen while speaking can be done onlyafter receiving entire input sequences. Thus, there is a significant delay whenencountering long utterances. By contrast, humans can listen to what hey speakin real-time, and if there is a delay in hearing, they won't be able tocontinue speaking. In this work, we propose an incremental machine speech chaintowards enabling machine to listen while speaking in real-time. Specifically,we construct incremental ASR (ISR) and incremental TTS (ITTS) by letting bothsystems improve together through a short-term loop. Our experimental resultsreveal that our proposed framework is able to reduce delays due to longutterances while keeping a comparable performance to the non-incremental basicmachine speech chain.

Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time