STYLER: Style Modeling with Rapidity and Robustness via SpeechDecomposition for Expressive and Controllable Neural Text to Speech

• URL: http://arxiv.org/abs/2103.09474v1
• Date: Wed, 17 Mar 2021 07:11:09 GMT
• Title: STYLER: Style Modeling with Rapidity and Robustness via SpeechDecomposition for Expressive and Controllable Neural Text to Speech
• Authors: Keon Lee, Kyumin Park, Daeyoung Kim
• Abstract summary: STYLER is a novel expressive text-to-speech model with parallelized architecture. Our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enabled style transfer without transferring noise.
• Score: 2.622482339911829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous works on expressive text-to-speech (TTS) have a limitation on robustness and speed when training and inferring. Such drawbacks mostly come from autoregressive decoding, which makes the succeeding step vulnerable to preceding error. To overcome this weakness, we propose STYLER, a novel expressive text-to-speech model with parallelized architecture. Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance. Moreover, our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enabled style transfer without transferring noise. Our experiments prove the naturalness and expressiveness of our model from comparison with other parallel TTS models. Together we investigate our model's robustness and speed by comparison with the expressive TTS model with autoregressive decoding.

Related papers

• Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM [48.71951982716363]
  Text-to-speech (TTS) models have been widely adopted to enhance automatic speech recognition (ASR) systems. We propose Hard- Synth, a novel ASR data augmentation method that leverages large language models (LLMs) and advanced zero-shot TTS. Our approach employs LLMs to generate diverse in-domain text through rewriting, without relying on additional text data.
  arXiv  Detail & Related papers  (2024-11-20T09:49:37Z)
• DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech [43.45691362372739]
  We propose a method called Directional Patch Interaction for Text-to-Speech (DPI-TTS) DPI-TTS employs a low-to-high frequency, frame-by-frame progressive inference approach that aligns more closely with acoustic properties. Experimental results demonstrate that our method increases the training speed by nearly 2 times and significantly outperforms the baseline models.
  arXiv  Detail & Related papers  (2024-09-18T09:36:55Z)
• SimpleSpeech 2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models [64.40250409933752]
  We build upon our previous publication by implementing a simple and efficient non-autoregressive (NAR) TTS framework, termed SimpleSpeech 2. SimpleSpeech 2 effectively combines the strengths of both autoregressive (AR) and non-autoregressive (NAR) methods. We show a significant improvement in generation performance and generation speed compared to our previous work and other state-of-the-art (SOTA) large-scale TTS models.
  arXiv  Detail & Related papers  (2024-08-25T17:07:39Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers [50.90457644954857]
  In this work, we apply diffusion models to approach sequence-to-sequence text generation. We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation. Experiment results illustrate the good performance on sequence-to-sequence generation in terms of text quality and inference time.
  arXiv  Detail & Related papers  (2022-12-20T15:16:24Z)
• Differentiable Duration Modeling for End-to-End Text-to-Speech [6.571447892202893]
  parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. We propose a differentiable duration method for learning monotonic sequences between input and output. Our model learns to perform high-fidelity synthesis through a combination of adversarial training and matching the total ground-truth duration.
  arXiv  Detail & Related papers  (2022-03-21T15:14:44Z)
• Advances in Speech Vocoding for Text-to-Speech with Continuous Parameters [2.6572330982240935]
  This paper presents new techniques in a continuous vocoder, that is all features are continuous and presents a flexible speech synthesis system. New continuous noise masking based on the phase distortion is proposed to eliminate the perceptual impact of the residual noise. Bidirectional long short-term memory (LSTM) and gated recurrent unit (GRU) are studied and applied to model continuous parameters for more natural-sounding like a human.
  arXiv  Detail & Related papers  (2021-06-19T12:05:01Z)
• Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis [76.39883780990489]
  We analyze the behavior of non-autoregressive TTS models under different prosody-modeling settings. We propose a hierarchical architecture, in which the prediction of phoneme-level prosody features are conditioned on the word-level prosody features.
  arXiv  Detail & Related papers  (2020-11-12T16:16:41Z)
• Pretraining Techniques for Sequence-to-Sequence Voice Conversion [57.65753150356411]
  Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody. We propose to transfer knowledge from other speech processing tasks where large-scale corpora are easily available, typically text-to-speech (TTS) and automatic speech recognition (ASR) We argue that VC models with such pretrained ASR or TTS model parameters can generate effective hidden representations for high-fidelity, highly intelligible converted speech.
  arXiv  Detail & Related papers  (2020-08-07T11:02:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.