Large Language Models Synthesize Speech: Challenges and Opportunities Revealed

Large language models (LLMs) have transformed the landscape of deep generative AI, enabling coherent and contextually rich content across diverse domains. In the realm of speech synthesis, LLMs have shown promise in generating natural-sounding speech from text inputs. However, current LLM-based text-to-speech (TTS) models face limitations, including hallucinations and attention errors that can lead to repeating words, missing words, and misaligned speech.

To overcome these challenges, researchers propose innovative techniques using CTC loss and attention priors to improve the robustness of LLM-based TTS systems. This article delves into the complexities of improving LLM-based speech synthesis and explores potential solutions for more natural and realistic speech generation.

Can Large Language Models Really Synthesize Speech?

The article explores the challenges of improving the robustness of large language model (LLM) based speech synthesis systems. These systems have demonstrated remarkable capabilities in handling large speech datasets and generating natural speech for new speakers. However, they are not robust as the generated output can contain repeating words, missing words, and misaligned speech, referred to as hallucinations or attention errors.

One of the main challenges is that LLM-based TTS models implicitly learn the text and speech alignment when trained for predicting speech tokens for a given text. This can lead to issues with monotonic cross-attention over the text tokens, which can result in repeating words, missing words, and misaligned speech.

How Do Large Language Models Learn Text and Speech Alignment?

Large language models (LLMs) learn text and speech alignment by implicitly learning the relationship between the input text and the output speech. This is done through the use of cross-attention mechanisms, which allow the model to attend to different parts of the input text and generate corresponding speech tokens.

However, this implicit learning can lead to issues with monotonic cross-attention over the text tokens. This means that the model may not always align the text and speech correctly, resulting in repeating words, missing words, and misaligned speech.

What are the Challenges of Large Language Model Based Speech Synthesis?

One of the main challenges of LLM-based speech synthesis is the lack of robustness in the generated output. The models can generate natural-sounding speech, but they may not always align the text and speech correctly, resulting in repeating words, missing words, and misaligned speech.

Another challenge is that LLM-based TTS models are typically trained on a specific dataset and may not generalize well to new speakers or unseen data. This means that the models may not be able to handle out-of-vocabulary words or unexpected pronunciation variations.

How Can We Improve the Robustness of Large Language Model Based Speech Synthesis?

To improve the robustness of LLM-based speech synthesis, researchers have proposed several techniques. One approach is to use a technique called “guided attention” training, which encourages the model to attend to specific parts of the input text and generate corresponding speech tokens.

Another approach is to use a loss function that penalizes the model for generating repeating words, missing words, or misaligned speech. This can help the model learn to align the text and speech more accurately and robustly.

What are the Benefits of Large Language Model Based Speech Synthesis?

Large language models (LLMs) have several benefits when it comes to speech synthesis. One benefit is that they can generate natural-sounding speech for new speakers, which can be useful in applications such as voice assistants or chatbots.

Another benefit is that LLMs can scale up to large speech datasets and be prompted in diverse ways to perform tasks like zero-shot speech synthesis or multilingual speech synthesis.

What are the Future Directions for Large Language Model Based Speech Synthesis?

One future direction for LLM-based speech synthesis is to explore new techniques for improving the robustness of the generated output. This could include using more advanced loss functions, such as those that penalize the model for generating repeating words, missing words, or misaligned speech.

Another future direction is to explore new applications for LLM-based speech synthesis, such as using it for voice assistants or chatbots.