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We present a voice conversion solution using recurrent sequence to sequencemodeling for DNNs. Our solution takes advantage of recent advances in attentionbased modeling in the fields of Neural Machine Translation (NMT),Text-to-Speech (TTS) and Automatic Speech Recognition (ASR). The problemconsists of converting between voices in a parallel setting when {\it$<$source,target$>$} audio pairs are available. Our seq2seq architecture makesuse of a hierarchical encoder to summarize input audio frames. On the decoderside, we use an attention based architecture used in recent TTS works. Sincethere is a dearth of large multispeaker voice conversion databases needed fortraining DNNs, we resort to training the network with a large single speakerdataset as an autoencoder. This is then adapted for the smaller multispeakervoice conversion datasets available for voice conversion. In contrast withother voice conversion works that use $F_0$, duration and linguistic features,our system uses mel spectrograms as the audio representation. Output mel framesare converted back to audio using a wavenet vocoder.

Hierarchical Sequence to Sequence Voice Conversion with Limited Data