TASU: Text-Only Alignment for Speech Understanding
Abstract
Recent advances in Speech Large Language Models (Speech LLMs) have paved the way for unified architectures across diverse speech understanding tasks. However, prevailing alignment paradigms rely heavily on large-scale audio-text paired data and computationally intensive training, yet often exhibit limited generalization to unseen domains or tasks. To address these limitations, we propose TASU (Text-only Alignment for Speech Understanding), a novel alignment paradigm that can leverage only unpaired text data to guide cross-modal alignment. Experiments show that TASU achieves competitive zero-shot speech recognition. Leveraging this property, it can further function as a pre-training stage in curriculum learning, enhancing domain generalization in speech recognition. Ultimately, TASU can extend its zero-shot generalization to a wide range of speech understanding tasks and notably outperforms prominent Speech LLMs including GLM-4-Voice and Step-Audio on the MMSU benchmark, establishing TASU as an efficient and scalable alignment paradigm for Speech LLMs.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of TASU, a novel alignment paradigm that enables effective speech understanding using only unpaired text data, significantly advancing the field of Speech LLMs. The technical contributions, particularly in methodology and experimental validation, position TASU as a promising approach for enhancing speech recognition capabilities while addressing existing limitations in data dependency.
Methodology
The proposed TASU method introduces innovative techniques such as a phone-synchronized disposal mechanism and a CTC topology simulation, which are particularly noteworthy for their ability to facilitate cross-modal alignment using only unpaired text data. This approach is a significant departure from traditional methods that rely on audio-text pairs, thus addressing a critical gap in the field. The methodology is well-structured, although further details on the implementation and optimization of these techniques would enhance clarity.
Results & evaluation
The experiments conducted demonstrate the effectiveness of TASU in achieving competitive zero-shot speech recognition and improving domain generalization. The comparison with existing Speech LLMs on the MMSU benchmark provides a solid basis for evaluating performance. However, more extensive datasets and diverse task evaluations could strengthen the findings.
Reproducibility
The paper lacks sufficient implementation details that would allow for easy reproduction of the results. While the methodology is promising, the absence of code or detailed experimental setups limits the ability of other researchers to validate the findings independently.
Limitations
One significant limitation is the reliance on text data alone, which may not capture the full complexity of speech signals. Additionally, the performance in truly unseen domains or tasks remains to be thoroughly evaluated, which could impact the generalizability of the proposed method.
Broader impact
The implications of TASU are substantial, particularly in making speech understanding technologies more accessible by reducing the need for large audio datasets. This could democratize advancements in speech recognition and understanding across various applications, from virtual assistants to accessibility tools. The main contribution of this paper is the introduction of TASU, a novel alignment paradigm that enables effective speech understanding using only unpaired text data, significantly advancing the field of Speech LLMs. The technical contributions, particularly in methodology and experimental validation, position TASU as a promising approach for enhancing speech recognition capabilities while addressing existing limitations in data dependency.
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