Text-Utilization for Encoder-dominated Speech Recognition Models

2026-04-29 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new paper explores efficient methods for integrating text-only data into encoder-dominated speech recognition models to enhance performance and speed. The research provides a detailed comparison of techniques, including modality matching and dynamic downsampling, to achieve text-level representations within the encoder. Experiments conducted on the LibriSpeech corpus reveal that configurations featuring a larger encoder and a smaller decoder can match or exceed the performance of models with larger decoders. The study also highlights that simpler approaches, such as random duration models, often prove more effective than complex alternatives, streamlining the training process. All associated code and recipes have been made publicly available.

Key takeaway

For research scientists developing speech recognition models, consider prioritizing encoder size over decoder size and exploring simpler text integration methods. Your focus on encoder-dominated architectures can lead to faster recognition without sacrificing accuracy. Evaluate random duration models as a straightforward yet effective approach to simplify your training pipelines and improve efficiency.

Key insights

Efficient text-only data integration improves encoder-dominated speech recognition, favoring simpler models and larger encoders.

Principles

• Larger encoders can compensate for smaller decoders.
• Simpler models often outperform complex alternatives.

Method

Integrate text-only data via modality matching and dynamic downsampling to achieve text-level representations within the encoder, using random duration models.

In practice

• Use random duration models for simpler training.
• Prioritize larger encoders for performance.

Topics

• Text-Utilization
• Speech Recognition
• Encoder-dominated Models
• Modality Matching
• Dynamic Downsampling

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.