Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences. Only the alignment paths that satisfy the monotonic condition are taken into consideration at each decoder timestep. The modified attention probabilities at each timestep are computed recursively using a forward algorithm. A transition agent for forward attention is further proposed, which helps the attention mechanism to make decisions whether to move forward or stay at each decoder timestep. Experimental results show that the proposed forward attention method achieves faster convergence speed and higher stability than the baseline attention method. Besides, the method of forward attention with transition agent can also help improve the naturalness of synthetic speech and control the speed of synthetic speech effectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Acoustic ModellingDecoderSpeech SynthesisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer
We propose a sequence-to-sequence singing synthesizer, which avoids the need for training data with pre-aligned phonetic and acoustic features. Rather than the more common approach of a content-based attention mechanism …
DecoderEnd-to-End Attention-based Large Vocabulary Speech Recognition
Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that d…
Acoustic ModellingLanguage ModelingLanguage Modellingspeech-recognition+1Acoustic-to-Word Recognition with Sequence-to-Sequence Models
Acoustic-to-Word recognition provides a straightforward solution to end-to-end speech recognition without needing external decoding, language model re-scoring or lexicon. While character-based models offer a natural solu…
Language ModelingLanguage Modellingspeech-recognitionSpeech RecognitionOverFlow: Putting flows on top of neural transducers for better TTS
Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech. They combine the best features of classic statistical speech synthesis and modern neural TTS, requiring …
Normalising FlowsSpeech Synthesistext-to-speechText to Speech+1Deep Recurrent Neural Networks for Acoustic Modelling
We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (D…
Acoustic ModellingAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition+1