💬 NLP

PROST-LLM: Progressively Enhancing the Speech-to-Speech Translation Capability in LLMs

The paper proposes PROST-LLM, a progressive framework that enhances the speech-to-speech translation capabilities of Large Language Models by combining tri-task fine-tuning on the CVSS corpus with self-generated preference optimization, effectively overcoming data scarcity challenges.

Original authors: Jing Xu, Jiaqi Wang, Daxin Tan, Xiao Chen

Published 2026-01-26

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CC BY 4.0

Original authors: Jing Xu, Jiaqi Wang, Daxin Tan, Xiao Chen

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a brilliant, multilingual librarian (a Large Language Model, or LLM) who can read and write text in almost any language. However, if you ask this librarian to listen to a spoken sentence in French and immediately speak back a translation in English, they stumble. They haven't practiced this specific "listening-to-speaking" dance enough, mostly because there aren't enough practice tapes available for them to study.

The paper introduces PROST-LLM, a training method designed to teach this librarian how to master that dance without needing a mountain of expensive, pre-recorded practice tapes or a human teacher to grade every single attempt.

Here is how PROST-LLM works, broken down into three simple steps using everyday analogies:

Step 1: The "Three-Act Play" (Supervised Fine-Tuning)

First, the researchers give the librarian a specific set of exercises using the CVSS corpus (a collection of speech data). Instead of just practicing the final goal (French speech → English speech), they use two clever tricks to build a stronger foundation:

The Tri-Task Play: Imagine the librarian is asked to perform three related acts in one show:
1. Transcribe: Listen to French speech and write it down.
2. Translate: Read French text and write it in English.
3. Translate Speech: Listen to French speech and speak English.
  By practicing all three at once, the librarian learns how the pieces fit together. Understanding the text helps them understand the speech, and vice versa.
The "Bridge" Strategy (Chain of Modality): Instead of trying to jump directly from "French Ear" to "English Mouth" (which is hard), the librarian is taught to take a tiny pause in the middle. They listen to French, think of the English words first, and then speak them. This "bridge" makes the transition smoother and more stable.

Step 2: The "Self-Reflection Mirror" (Preference Data Construction)

Now the librarian has some basic skills, but they still need to learn which answers are better than others. Usually, you would need a human expert to listen to two translations and say, "A is better than B." But that is slow and expensive.

PROST-LLM uses a Back-Translation Mirror to do this automatically:

The librarian generates two different English translations for a French sentence.
The librarian then takes those English translations and translates them back into French.
The Comparison: The system compares the "re-translated" French against the original French.
- If the librarian's English translation was good, translating it back will sound very close to the original French.
- If the translation was bad, the "mirror" will show a distorted, different French sentence.

The system automatically picks the "winner" (the one that looked most like the original when reflected back) and the "loser." This creates a list of "Good vs. Bad" examples without a single human ever having to listen to them.

Step 3: The "Taste Test" (Preference Optimization)

Finally, the librarian studies this list of winners and losers. Using a technique called Preference Optimization (like DPO), the librarian learns to prefer the style of speaking that leads to the "winning" result.

Think of this as a chef tasting their own cooking. Instead of waiting for a food critic to tell them if the soup is too salty, they taste it, compare it to a perfect recipe, and adjust their seasoning for next time. This step fine-tunes the model to speak more naturally and accurately.

The Results: What Did They Find?

The paper claims that this method works very well:

Closing the Gap: Before this method, "End-to-End" systems (one brain doing everything) were much worse than "Cascaded" systems (one brain for listening, another for translating, another for speaking). PROST-LLM shrinks that performance gap significantly, making the single-brain system almost as good as the complex three-brain system.
Less Data Needed: Because the system can use the "mirror" trick on monolingual data (just French speech or just English speech), it doesn't need as many expensive, perfectly matched French-to-English pairs to learn.
Better Quality: The translations sound more natural to human ears (measured by a score called UTMOS) and are more accurate (measured by BLEU scores).

In short: PROST-LLM teaches a text-smart AI how to speak and listen by having it practice related tasks, using a "mirror" to grade its own work, and then refining its skills based on those self-grades. This allows the AI to become a great translator without needing a massive library of human-graded examples.

Technical Summary: PROST-LLM

Problem Statement

While Large Language Models (LLMs) have demonstrated remarkable potential in various domains, their application to Speech-to-Speech Translation (S2ST) remains under-explored. The primary barrier is the scarcity of paired S2ST data. Conventional S2ST systems typically rely on cascaded pipelines (ASR + MT + TTS) or complex end-to-end architectures that scale poorly. Although Preference Optimization (PO) has proven effective for aligning LLMs in Natural Language Processing (NLP) tasks, its potential for improving S2ST performance has not been studied. Furthermore, existing PO methods often rely on costly human feedback or high-quality preference pairs, which are difficult to obtain for S2ST tasks.

Methodology: PROST-LLM

The authors propose PROST-LLM (PROgressive Speech-to-speech Translation), a framework designed to progressively enhance S2ST capabilities in LLMs through three distinct stages:

1. Supervised Fine-Tuning (SFT)

The pre-trained LLM is first fine-tuned on the CVSS corpus to acquire initial S2ST abilities. To address data scarcity and improve task understanding, two specific strategies are employed:

Tri-task Learning: The S2ST corpus is converted into parallel Automatic Speech Recognition (ASR) and Speech-to-Text (S2T) data. The model is jointly trained on ASR, S2T, and S2ST tasks using task-specific instructions to encourage cross-task knowledge transfer.
Chain of Modality: To simplify direct speech-to-speech generation, the model is instructed to first generate the target text and then produce the target speech units within a single forward pass. This sequential structure facilitates cross-modal learning and improves generation stability.
Architecture: The system utilizes a speech tokenizer (discretizing SSL-extracted features via K-means), an LLM backbone (LLaMA 3.2-3B), and a speech de-tokenizer (HiFi-GAN).

2. Preference Data Construction

To eliminate the need for costly human annotations and large-scale paired data, the authors introduce an automated preference pair construction method:

Self-Sampling & Back-Translation: The fine-tuned model generates multiple translation candidates for a source speech input. These candidates are then back-translated into the source language speech using the same model.
Automatic Assessment: The quality of the translation is assessed by comparing the back-translated speech with the original source speech. Metrics include acoustic measures (Mel-Cepstral Distortion - MCD), text-based metrics (Word Error Rate - WER), and translation metrics (BLEU, METEOR).
Pair Generation: Candidates are scored, and pairs are formed where a "preferred" candidate ( $S^p_B$ ) significantly outperforms a "rejected" candidate ( $S^r_B$ ) based on a margin hyper-parameter ( $\delta$ ). This process allows the use of monolingual speech corpora for constructing preference pairs.

3. Preference Optimization (PO)

The constructed preference pairs are used to further refine the model via Direct Preference Optimization (DPO). The objective function maximizes the margin between the preferred and rejected translations, enhancing the model's ability to distinguish and generate high-quality speech translations without requiring human feedback.

Key Contributions

The paper claims the following contributions:

First Framework for LLM-based S2ST: It presents the first framework to equip LLMs with S2ST capabilities using tri-task learning and chain-of-modality strategies.
Novel Application of PO in S2ST: It is the first work to apply preference optimization to enhance S2ST capabilities using preference pairs constructed via back-translation, removing the reliance on human evaluation.
Reduced Data Dependency: By utilizing monolingual speech corpora to construct preference pairs, the method significantly reduces the reliance of LLMs on scarce paired S2ST data.
Performance Validation: Experiments demonstrate that PROST-LLM effectively improves S2ST quality, narrowing the performance gap between end-to-end systems and cascaded baselines.

Experimental Results

Experiments were conducted on the CVSS corpus (English-French translation) using the LLaMA 3.2-3B backbone.

Baseline Comparison: Both tri-task learning and chain-of-modality strategies outperformed a "Vanilla" LLM-based S2ST baseline. The Chain of Modality approach yielded the most significant gains, effectively bridging the speech-text modality gap.
Impact of Preference Optimization: Applying PO (DPO) provided additional performance boosts.
- On the CVSS-C dataset, the BLEU gap between the PROST-LLM (Chain of Modality) and the cascaded system narrowed from 14.38 to 3.15 (en2fra) and from 8.83 to 1.04 (fra2en).
- The system also achieved higher UTMOS scores (indicating more natural speech) compared to the cascaded baseline.
Monolingual vs. Paired Data: Models trained on preference pairs derived from monolingual corpora (Commonvoice) outperformed those trained on paired S2ST data, further reducing the BLEU gap to 1.65 (en2fra) and 1.2 (fra2en).
Robustness: The approach remained effective across different back-translation metrics (WER, MCD, BLEU, METEOR) and different PO algorithms (DPO and SimPO).
Efficiency: The system showed strong performance even with limited data (e.g., 2,500 samples) and single-iteration training, suggesting high efficiency.

Significance

The paper asserts that PROST-LLM offers a scalable and effective solution for S2ST in LLMs. By leveraging back-translation for automatic preference construction, the framework overcomes the critical bottleneck of data scarcity and the high cost of human annotation. The results validate that LLMs can be effectively adapted for S2ST tasks through progressive enhancement, achieving performance levels that closely approach traditional cascaded systems while utilizing a simpler architecture and less paired data. Future work is noted to extend this framework to more languages and larger monolingual resources.