Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs

2026-07-07

AI Summary

This work addresses the limited word-level time alignment capability of current automatic speech recognition (ASR) models—such as attention-based encoder-decoder (AED) systems and speech large language models—which often lack precise temporal grounding, while conventional alignment methods are constrained by encoder frame rates and offer only modest accuracy. The authors propose a general, training-free, and model-agnostic gradient-driven alignment approach that computes frame-level saliency maps via gradients of token log-probabilities with respect to the input signal under teacher forcing, followed by dynamic programming to decode word boundaries. Applicable to any differentiable ASR model, this method achieves high-precision alignment at the original input sampling rate. Experiments across 16 models on TIMIT and Buckeye datasets show that, although slightly less accurate than strong native aligners, it outperforms them in scenarios where native alignment capabilities are weak, such as with streaming ASR models.

Abstract

Speech-to-text alignment means finding the temporal boundaries of each word in the audio. Some models provide such an alignment directly and others do not. Connectionist temporal classification (CTC) and transducer models have an alignment by construction, whereas attention-based encoder-decoders (AED) and speech large language models (LLMs) do not, and their word timings are usually read off the attention weights instead. All of these signals live on the encoder frame grid, which bounds their temporal precision. We study a generic gradient-based alignment that applies to any differentiable ASR model. We take the gradient of each teacher-forced token log probability with respect to the input, reduce it to a per-frame saliency, and decode the resulting matrix into word boundaries with a single dynamic-programming pass. The method needs no training, no model modification and no alignment heads, works across all model families including the speech LLMs, and aligns on the input grid rather than on the coarser encoder grid. We evaluate it on sixteen models from four families, on read (TIMIT) and spontaneous (Buckeye) speech, each against the model's own native or attention-based alignment. We find that the gradient yields a usable alignment for every model, that it is usually somewhat behind a strong native aligner but better where the native alignment is weak, as for the streaming models, and that its main disadvantage is the cost of one backward pass per token.

Problem

• speech-to-text alignment
• automatic speech recognition
• temporal boundaries
• encoder frame grid
• speech LLMs

Innovation

• gradient-based alignment
• speech-to-text alignment
• automatic speech recognition
• speech LLMs
• frame-level saliency