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We present a direct simultaneous speech-to-speech translation (Simul-S2ST)model, Furthermore, the generation of translation is independent fromintermediate text representations. Our approach leverages recent progress ondirect speech-to-speech translation with discrete units, in which a sequence ofdiscrete representations, instead of continuous spectrogram features, learnedin an unsupervised manner, are predicted from the model and passed directly toa vocoder for speech synthesis on-the-fly. We also introduce the variationalmonotonic multihead attention (V-MMA), to handle the challenge of inefficientpolicy learning in speech simultaneous translation. The simultaneous policythen operates on source speech features and target discrete units. We carry outempirical studies to compare cascaded and direct approach on the FisherSpanish-English and MuST-C English-Spanish datasets. Direct simultaneous modelis shown to outperform the cascaded model by achieving a better tradeoffbetween translation quality and latency.

Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention