RALL-E: A Robust Codec Language Model with Chain-of-Thought Prompting for Improved Text-to-Speech Synthesis

Core Concepts

RALL-E improves the robustness of large language model-based text-to-speech synthesis by incorporating prosody tokens as chain-of-thought prompting and using duration-guided masking to enhance the alignment between phonemes and speech tokens.

Abstract

The paper presents RALL-E, a robust codec language modeling method with chain-of-thought (CoT) prompting for text-to-speech (TTS) synthesis. The key ideas are:

Prosody tokens (pitch and duration) are predicted as intermediate results before generating speech tokens, using CoT prompting to enhance the robustness of the language model-based TTS.
Duration-guided masking is used to enforce the model to focus on the corresponding phonemes and prosody features when predicting speech tokens, improving the alignment between text and speech.

The authors conduct comprehensive objective and subjective evaluations, demonstrating that RALL-E significantly outperforms the baseline VALL-E method and two previous works in terms of word error rate, speech naturalness, and robustness on hard sentences. Specifically:

RALL-E reduces the word error rate from 6.3% (without reranking) and 2.1% (with reranking) to 2.8% and 1.0%, respectively, on the LibriSpeech test set.
On 50 particularly hard sentences, RALL-E reduces the error rate from 68% to 4%, closely approaching the performance of a non-autoregressive TTS method.
The ablation study demonstrates the effectiveness of each component in RALL-E, with the CoT prompting being the most important.

Stats

The training data is the English subset of the multilingual LibriSpeech corpus, containing about 44K hours of speech data from 5490 speakers.
The test set is the test-clean set of the LibriSpeech corpus, containing 1205 utterances.

Quotes

"The core idea of RALL-E is inspired from the chain-of-thought (CoT) prompting. In CoT prompting, the LLM is instructed to generate an intermediate result that is used as a condition for the prediction of the final result."
"RALL-E first predicts phoneme-level pitch and duration of the input, then predicts speech tokens conditioning on both the input phonemes and the predicted prosody tokens."
"RALL-E utilizes the predicted duration to mask irrelevant phonemes and prosody tokens when computing self-attention weights, so that the codec language model is enforced to concentrate on tokens around the phoneme and prosody token the speech token corresponds to."

Key Insights Distilled From

RALL-E

by Detai Xin,Xu... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03204.pdf

Deeper Inquiries

How can the proposed CoT prompting and duration-guided masking be extended to other autoregressive language model-based tasks beyond text-to-speech synthesis

The proposed Chain-of-Thought (CoT) prompting and duration-guided masking techniques in RALL-E can be extended to other autoregressive language model-based tasks beyond text-to-speech synthesis by adapting the core principles to suit the specific requirements of the new tasks.
For tasks like machine translation, the CoT prompting can be utilized to break down the translation process into simpler steps. The model can first predict intermediate linguistic features or representations before generating the final translated text. This can help improve the alignment between source and target languages and enhance the overall translation quality.
Similarly, in text generation tasks, the CoT prompting can guide the model to generate coherent and contextually relevant text by predicting intermediate prompts or features. By incorporating duration-guided masking, the model can focus on specific parts of the input text or context to ensure accurate and consistent generation.
Overall, the key is to tailor the CoT prompting and duration-guided masking techniques to the specific requirements and characteristics of each autoregressive language model-based task, thereby enhancing robustness and performance across a variety of applications.

What are the potential limitations of the current RALL-E approach, and how could it be further improved to handle more challenging cases or different languages

While RALL-E has shown significant improvements in the robustness of text-to-speech synthesis, there are potential limitations that could be addressed for handling more challenging cases or different languages.
One limitation is the reliance on predicted duration values, which may not always accurately correspond to the actual duration of speech tokens. This could lead to misalignments and errors in the synthesized speech. Improvements in the duration prediction mechanism, such as incorporating feedback loops or reinforcement learning, could enhance the accuracy of duration predictions.
Additionally, the current approach may struggle with complex linguistic structures, dialects, or languages with unique phonetic characteristics. Adapting the model to handle these variations through multi-task learning, transfer learning, or data augmentation techniques could improve performance across diverse language contexts.
Furthermore, exploring the integration of prosody features beyond pitch and duration, such as intonation patterns or emphasis markers, could further enhance the naturalness and expressiveness of the synthesized speech.
To address these limitations and improve the RALL-E approach, future research could focus on refining the duration prediction mechanism, enhancing model adaptability to diverse linguistic contexts, and expanding the scope of prosody features considered in the synthesis process.

Given the success of RALL-E in improving the robustness of text-to-speech synthesis, how could the insights from this work be applied to enhance the robustness of other generative language models, such as those used for text generation or machine translation

The insights from the success of RALL-E in improving the robustness of text-to-speech synthesis can be applied to enhance the robustness of other generative language models, such as those used for text generation or machine translation.
For text generation tasks, incorporating CoT prompting can help generate more coherent and contextually relevant text by guiding the model through intermediate steps. By predicting key linguistic features or prompts before generating the final text, the model can maintain consistency and coherence in the generated output.
In machine translation, the principles of duration-guided masking can be adapted to improve alignment between source and target languages. By focusing on specific parts of the input text or context during translation, the model can ensure accurate and contextually appropriate translations.
Overall, the insights from RALL-E can inform the development of more robust and reliable generative language models across a range of applications, by emphasizing the importance of intermediate steps, alignment mechanisms, and context-aware generation techniques.