Cascaded Modular or End-to-End? : An Investigation on Speech-to-Speech Translation Task for Dravidian Languages

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing & Speech Technology, Data Science & Analytics · Depth: Expert, quick

Summary

A study investigated speech-to-speech translation for low-resource Dravidian languages, specifically Tamil, Telugu, and Kannada, comparing Cascaded Modular and End-to-end systems. The Cascaded Modular approach integrated Whisper-based ASR for English, IndicConformer for Dravidian speech, IndicTrans2 for text-to-text translation, and Indic Parler-TTS for speech synthesis. SeamlessM4T served as the End-to-end system. Researchers evaluated both systems in zero-shot and fine-tuned settings, utilizing Low-Rank Adaptation (LoRA) with FLEURS and Mann-ki-Baat datasets. Cascaded Modular systems achieved BLEU scores ranging from 3.17 to 19.18 in zero-shot and 5.08 to 19.18 after fine-tuning. In contrast, the End-to-end model scored 3.02 to 15.72 in zero-shot and 4.11 to 16.84 post-fine-tuning. The Cascaded Modular systems consistently outperformed the End-to-end model across all setups, with parameter-efficient fine-tuning yielding significant improvements in translation quality and speech generation.

Key takeaway

For NLP Engineers developing speech-to-speech translation systems for low-resource languages like Dravidian, you should prioritize a Cascaded Modular architecture over an End-to-end approach. This study demonstrates its superior performance, especially when combined with parameter-efficient fine-tuning using techniques like LoRA. Consider integrating specialized modules such as IndicTrans2 and Indic Parler-TTS for optimal results, as this strategy significantly improves translation quality and speech generation for challenging language pairs.

Key insights

Cascaded Modular systems consistently outperform End-to-end models for low-resource Dravidian speech-to-speech translation.

Principles

• Modular architectures can exceed monolithic end-to-end systems.
• Parameter-efficient fine-tuning significantly enhances low-resource translation.
• Domain-specific adaptation improves speech translation quality.

Method

The study compared Cascaded Modular (ASR, Text-to-Text, TTS modules) with End-to-end (SeamlessM4T) systems, evaluating zero-shot and LoRA fine-tuning on FLEURS and Mann-ki-Baat datasets for Dravidian languages.

In practice

• Prioritize modular systems for low-resource speech translation.
• Apply LoRA fine-tuning for domain adaptation.
• Utilize IndicTrans2 and Indic Parler-TTS for Dravidian languages.

Topics

• Speech-to-Speech Translation
• Dravidian Languages
• Low-Resource NLP
• Cascaded Modular Systems
• LoRA Fine-tuning
• SeamlessM4T

Best for: Research Scientist, AI Engineer, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.