  Despite the close relationship between speech perception and production,
research in automatic speech recognition (ASR) and text-to-speech synthesis
(TTS) has progressed more or less independently without exerting much mutual
influence on each other. In human communication, on the other hand, a
closed-loop speech chain mechanism with auditory feedback from the speaker's
mouth to her ear is crucial. In this paper, we take a step further and develop
a closed-loop speech chain model based on deep learning. The
sequence-to-sequence model in close-loop architecture allows us to train our
model on the concatenation of both labeled and unlabeled data. While ASR
transcribes the unlabeled speech features, TTS attempts to reconstruct the
original speech waveform based on the text from ASR. In the opposite direction,
ASR also attempts to reconstruct the original text transcription given the
synthesized speech. To the best of our knowledge, this is the first deep
learning model that integrates human speech perception and production
behaviors. Our experimental results show that the proposed approach
significantly improved the performance more than separate systems that were
only trained with labeled data.
