Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?

Abstract: The field of NLP has recently witnessed a transformative shift with the emergence of foundation models, particularly LLMs that have revolutionized text-based NLP. This paradigm has extended to other modalities, including speech, where researchers are actively exploring the combination of Speech Foundation Models (SFMs) and LLMs into single, unified models capable of addressing multimodal tasks. Among such tasks, this paper focuses on speech-to-text translation (ST). By examining the published papers on the topic, we propose a unified view of the architectural solutions and training strategies presented so far, highlighting similarities and differences among them. Based on this examination, we not only organize the lessons learned but also show how diverse settings and evaluation approaches hinder the identification of the best-performing solution for each architectural building block and training choice. Lastly, we outline recommendations for future works on the topic aimed at better understanding the strengths and weaknesses of the SFM+LLM solutions for ST.

• The paper systematically surveys architectures integrating SFMs and LLMs for speech-to-text translation, detailing components like length and modality adapters.
• It reveals significant variability in training data, finetuning, and evaluation practices, stressing the need for standardized benchmarks and comparative analyses.
• The study outlines future research directions focusing on in-context learning, cross-modal capabilities, and enhanced evaluation metrics for translation quality.

Speech Translation with Speech Foundation Models and LLMs: Overview and Insights

The paper "Speech Translation with Speech Foundation Models and LLMs: What is There and What is Missing?" explores the landscape of combining Speech Foundation Models (SFMs) and LLMs for speech-to-text translation (ST). This investigation is set within a broader context of multimodal foundation models, marking a significant shift in NLP paradigms, especially as multimodal tasks become more prevalent.

Architectural Insights

The paper systematically surveys existing architectural solutions that integrate SFMs and LLMs to facilitate speech-to-text translation. The authors uncover a common structural framework comprising five key components:

1. Speech Foundation Model (SFM): These models extract high-level semantic representations from audio inputs. The surveyed works employ a variety of SFMs, such as wav2vec and Whisper, although there is a notable lack of consensus or comparative evaluations of their performance.
2. Length Adapter (LA): This component compresses the lengthy audio sequences into more manageable representations aligned with the LLM input requirements. Different techniques for this compression have been proposed, but a systematic comparison under varying conditions is lacking.
3. Modality Adapter (MA): Post-length adaptation, the modality adapter maps the reduced audio representation into a compatible space for the LLM. The necessity and complexity of this component are often dictated by the training strategy applied to the LLM.
4. Prompt-Speech Mixer (PSMix): This module merges textual prompts with the adapted speech representation before input into the LLM. The prompt structures and mixing strategies vary widely among implementations without clear evidence of the optimal configuration.
5. LLM: Finally, the LLM processes mixed inputs to generate fluent text translations. The surveyed studies commonly use models from the LLaMA family, with ongoing interest in how translation-specific LLMs might enhance performance.

Training and Evaluation Practices

The paper identifies significant variability and lack of standardization in training data and evaluation practices, which pose challenges for direct comparison across diverse methodologies:

• Training Data: There is no uniformity in dataset selection and size for ST tasks, with some studies relying on publicly available corpora while others utilize private resources.
• Training Tasks: Beyond ST, models are trained on additional tasks like ASR or SQA. However, the effect of such multitask training on ST performance remains underexplored.
• Finetuning Practices: Both SFMs and LLMs exhibit varied finetuning strategies. Notably, LLM finetuning appears critical for performance improvement, though optimal configurations remain to be determined.
• Evaluation Metrics and Datasets: Most studies rely on BLEU scores using datasets like MuST-C and CoVoST2. There is a call for broader adoption of semantic metrics like COMET to overcome BLEU's limitations in evaluating LLM-generated translations.

Future Directions and Implications

The authors recommend several avenues for advancing research in this domain:

• Standardized Training and Evaluation Frameworks: Establishing public and consistent benchmark datasets would facilitate fair comparisons and bolster collaborative advances.
• Comparative Analysis with Traditional Models: The relative advantages over existing end-to-end and cascade ST systems need systematic evaluation, considering factors like efficiency and translation quality.
• In-Context Learning and Cross-Modal Capabilities: Understanding how SFMs and LLMs can collaborate in leveraging in-context learning abilities for ST tasks offers an exciting frontier for research.

In conclusion, the integration of SFMs and LLMs in speech translation represents a promising evolution in NLP technology. However, realizing their full potential necessitates resolving existing uncertainties through coordinated research efforts focused on standardization, comprehensive evaluations, and cross-disciplinary competencies.