Text-Utilization for Encoder-dominated Speech Recognition Models

Problem
This preprint addresses the gap in leveraging text-only data to enhance the performance of encoder-dominated speech recognition models. While existing literature primarily focuses on audio data for training, the authors explore the potential of integrating text data to improve recognition accuracy and efficiency. The study aims to identify effective methods for utilizing text data, particularly in scenarios where audio data may be limited or costly to obtain.

Method
The authors propose several techniques for integrating text-only data into encoder-dominated architectures. Key contributions include modality matching and dynamic downsampling strategies that facilitate the extraction of text-level representations within the encoder. The experiments utilize the LibriSpeech corpus, a standard benchmark for speech recognition tasks. The authors evaluate various configurations, emphasizing the performance of larger encoders paired with smaller decoders. They also investigate the efficacy of simpler models, such as random duration models, which demonstrate competitive performance compared to more complex architectures. The training pipeline is simplified, allowing for efficient model training without extensive computational resources.

Results
The experiments reveal that models with larger encoders and smaller decoders can achieve performance metrics that equal or exceed those of traditional architectures with larger decoders. Specifically, the proposed configurations yield a Word Error Rate (WER) reduction of up to 10% compared to baseline models on the LibriSpeech test set. The random duration models, despite their simplicity, outperform several complex alternatives, indicating that effective utilization of text data can lead to significant improvements in speech recognition tasks. The authors provide detailed comparisons against established baselines, demonstrating the robustness of their approach.

Limitations
The authors acknowledge several limitations in their work. First, the reliance on the LibriSpeech corpus may restrict the generalizability of the findings to other datasets or languages. Additionally, while the integration of text data shows promise, the paper does not explore the potential impact of varying text data quality or domain specificity on model performance. The authors also note that the simplification of the training pipeline may not be universally applicable across all speech recognition tasks, particularly those requiring more nuanced audio features.

Why it matters
This research has significant implications for the development of more efficient speech recognition systems, particularly in resource-constrained environments. By demonstrating that text-only data can effectively enhance encoder-dominated models, the findings encourage further exploration of multimodal training approaches. The simplification of training processes may also lower the barrier to entry for developing advanced speech recognition systems, making them more accessible to researchers and practitioners. The availability of code and recipes promotes reproducibility and encourages further experimentation in the field.

Authors: Albert Zeyer, Tim Posielek, Ralf Schlüter, Hermann Ney
Source: arXiv:2604.26514
URL: https://arxiv.org/abs/2604.26514v1