Tacotron: Towards End-to-End Speech Synthesis
Abstract
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
Tacotron presents a groundbreaking end-to-end generative text-to-speech model that synthesizes speech directly from characters, achieving high naturalness scores and simplifying the TTS pipeline. The technical contributions, particularly the innovative architecture and effective evaluation methods, position Tacotron as a significant advancement in the field of speech synthesis.
Methodology
The methodology presented in Tacotron is innovative as it integrates a sequence-to-sequence model with attention mechanisms to create an end-to-end text-to-speech synthesis system. The use of character-level inputs and the elimination of the need for phoneme-level alignment represent significant advancements in simplifying the TTS pipeline. The introduction of the CBHG module and the post-processing network further enhances the model's performance by improving feature extraction and reducing synthesis artifacts. The model's ability to be trained from scratch with random initialization is a notable strength, allowing for scalability and adaptability to various datasets.
Results & evaluation
The experimental evaluation is robust, featuring a well-defined dataset of 24.6 hours of speech data and a thorough mean opinion score (MOS) assessment that demonstrates Tacotron's superiority over existing parametric systems. The ablation studies provide insight into the contributions of different components of the model, reinforcing the effectiveness of the proposed architecture. The reliance on subjective metrics like MOS, combined with the visual alignment comparisons, adds depth to the evaluation.
Reproducibility
The paper provides sufficient details on the model architecture, training procedures, and hyperparameters, which supports reproducibility. However, the lack of a publicly available code repository limits the ability for other researchers to replicate the results directly. The implementation in TensorFlow is a positive aspect, as it is a widely used framework, but the absence of a project URL is a drawback.
Limitations
One limitation is the reliance on the Griffin-Lim algorithm for waveform synthesis, which is known to produce artifacts. The paper acknowledges this and suggests ongoing work to develop a more advanced neural-network-based spectrogram inverter. Additionally, the model's performance is evaluated only on a single language (US English), which may limit its generalizability to other languages and dialects.
Broader impact
The potential applications of Tacotron are significant, ranging from enhancing accessibility through improved speech synthesis for visually impaired individuals to creating more natural-sounding virtual assistants and voiceovers in media. The integration of TTS technology into various consumer products could lead to more engaging user experiences and broaden the reach of automated communication systems. Tacotron presents a groundbreaking end-to-end generative text-to-speech model that synthesizes speech directly from characters, achieving high naturalness scores and simplifying the TTS pipeline. The technical contributions, particularly the innovative architecture and effective evaluation methods, position Tacotron as a significant advancement in the field of speech synthesis.
Curator note
Wang et al., Google; seq2seq TTS from text to mel-spectrogram; replaced pipeline TTS
Related later work
- Tacotron 2 2017
- FastSpeech 2019
- VITS 2021
- GlowTTS 2020
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