Source-linked AI summary
Relative Time Intervals Representation for Word-level Timestamping with Masked Training
Quanwei Tang, Zhiyu Tang, Xu Li, Dong Zhang, Shoushan, Guodong Zhou
TL;DR
Fine-grained temporal alignment remains underexplored in SpeechLLMs despite their speech capabilities. The paper introduces relative timestamps, hybrid fine-tuning, and masked training, reporting improved timestamp accuracy while maintaining strong transcription performance.
Problem
Fine-grained word-level timestamped transcription remains underexplored for SpeechLLMs and requires precise alignment between linguistic units and acoustic realizations.
Method
The paper replaces absolute timestamps with relative intervals and combines hybrid fine-tuning, joint temporal alignment loss, and timestamp masking.
Results
The Relative Timestamp method achieved the highest Precision and Recall across tolerance levels on LibriSpeech and Wenet-Meeting, while also outperforming baselines in WER.
Takeaways & Limitations
The approach upgrades speech models toward jointly modeling speech content and temporal structure with compact, generalizable temporal representations.
Abstract
from arXiv · showhide
Although Speech Large Language Models (SpeechLLMs) excel at speech understanding and generation, their capacity for fine-grained, temporally aligned outputs remains underexplored. Our work addresses this gap by enabling SpeechLLMs to jointly model speech content and temporal structure, effectively transforming them from ``content understanding machines" into ``temporal-aware content understanding machines". Specifically, we replace traditional absolute timestamps with relative timestamps, achieving a more compact vocabulary and stronger generalization capabilities. To efficiently infuse timestamp prediction ability into pre-trained large language models, we introduce a hybrid fine-tuning strategy: full-parameter fine-tuning of the timestamp-augmented embedding layer and language model head, combined with LoRA fine-tuning of the decoder layers. Moreover, we design a masked timestamp training objective, preventing the model from over-relying on ground-truth timestamps, and thereby enhancing robustness against noisy real-world annotations. Extensive experiments demonstrate that our approach achieves significant improvements in timestamp prediction accuracy while maintaining strong speech transcription performance.
1. INTRODUCTION
The paper addresses fine-grained word-level timestamping by replacing absolute timestamps with relative intervals and combining masking, alignment, and hybrid fine-tuning strategies.
- Word-level timestamped transcription requires both accurate speech understanding and precise temporal alignment, but remains underexplored for SpeechLLMs.
- Absolute timestamps accumulate errors, generalize poorly beyond training durations, and require prohibitively large vocabularies for high temporal precision.At 0.01-second resolution over 300 seconds, the representation requires 30,000 distinct timestamp tokens.
- Relative timestamps represent the interval between consecutive words, limiting the learned range while cumulatively representing audio of any length.The proposed interval example uses 0.65 seconds between words aligned at 0.45 and 1.10 seconds.
- Timestamp Masking and a dynamically weighted Joint Temporal Alignment Loss regularize training and balance transcription accuracy with timestamp fidelity.Timestamp masking is intended to mitigate overfitting to perfect alignments.
- Hybrid fine-tuning fully updates timestamp-specific embedding and output modules while adapting decoder layers with LoRA.This strategy preserves linguistic knowledge while limiting updates to pre-trained decoder parameters.
2. METHODOLOGY
The methodology uses relative intervals, a hybrid adaptation strategy, joint text-and-timestamp optimization, and timestamp masking to train temporal alignment efficiently and robustly.
- 2.1. Relative Timestamp Representation: Each relative timestamp token represents the interval from the preceding word’s end to the current word, enabling compact vocabulary and arbitrary audio lengths.
- 2.2. Hybrid Fine-Tuning: The model uses a pre-trained speech encoder-decoder architecture, adapting it for temporal alignment with hybrid fine-tuning.
- 2.2. Hybrid Fine-Tuning: Timestamp embeddings and the LM head receive full-parameter updates because they learn the newly introduced temporal vocabulary from random initialization.
- 2.2. Hybrid Fine-Tuning: LoRA adapts decoder layers by freezing original weights and adding trainable low-rank matrices, reducing trainable parameters and computational cost.
- 2.3.1. Joint Temporal Alignment Loss: The joint objective combines text-transcription and timestamp cross-entropy losses as Ltotal = Ltext + λLtimestamp.Ltext evaluates text-token prediction, while Ltimestamp evaluates discrete timestamp-token prediction.
- 2.3.1. Joint Temporal Alignment Loss: The weighting factor λ starts at 1 and increases by 1 each epoch, progressively emphasizing temporal alignment.
- 2.3.2. Timestamp Masking for Regularization: Timestamp masking randomly replaces timestamp tokens with [MASK], forcing inference from speech content and historical tokens rather than ground-truth timestamps.The technique is designed to improve robustness to inaccurate annotations and prevent reliance on specific timestamps.
3. EXPERIMENTS
Experiments evaluate timestamp prediction and transcription across multiple datasets, baselines, and ablations. Relative Timestamp achieves leading timestamp metrics and consistently strong WER, while timestamp loss and masking materially affect performance.
- Experimental Setup: Experiments compare timestamp prediction across LibriSpeech and Wenet-Meeting using tolerance-based Precision, Recall, and Average Time Difference metrics.The study also evaluates WER across five datasets and compares multiple baseline models.
- Main Results: 91.13% Precision and 86.88% Recall at 240 ms tolerance were achieved on Wenet-Meeting, with a 30.34 ms Average Time Difference.These were the strongest reported timestamp results in the comparison.
- Main Results: Relative Timestamp achieved the highest Precision and Recall across all tolerance levels on both evaluated timestamping datasets.Canary performed slightly better at high tolerance levels but worse at low tolerance levels, while Qwen2-Audio nearly failed on LibriSpeech.
- Ablation Study: Removing timestamp loss increased WER and decreased Precision and Recall, indicating that the loss guides timestamp prediction alongside text generation.The ablation identifies timestamp loss as important for learning accurate temporal outputs.
- Ablation Study: Removing the timestamp mask increased WER from 2.15% to 2.56% on AISHELL-2 iOS and from 11.63% to 14.47% on Common Voice.The reported ablation attributes this degradation to reduced robustness and weaker focus on valid timestamp predictions.
- Analysis of ASR: Relative Timestamp produced the best WER on all five datasets, including 1.26% on AISHELL-1, 2.15% on AISHELL-2, and 5.56% on Wenet Meeting.It also achieved 2.78% WER on LibriSpeech and 11.63% on Common Voice.
4. CONCLUSION
The paper proposes a framework for precise temporal sequence understanding in speech models, extending beyond timestamp tags toward aligned multimodal spatiotemporal understanding.
- 4. CONCLUSION: The framework aims to equip speech language models with fine-grained, aligned multimodal spatiotemporal understanding.It is presented as a shift from content understanding toward temporal-aware content understanding.