The sequence-to-sequence model has emerged as a potent tool in speech translation. It can
train end-to-end to efficiently map speech spectrograms in one language to their corresponding
spectrograms in another. The notable advantage of this approach is eliminating the need for an
intermediate text representation, resulting in improved efficiency. Additionally, the Seq2seq models
have been successfully implemented in speech generation tasks, where they reverse the ASR
approach. In such applications, the input text sequence serves as the input, with the encoder
network creating a feature vector representation of the input text. The decoder network then
leverages this representation to generate the desired speech signal.
AReviewofDeepLearningTechniquesforSpeechProcessing