ZeroSyl: Simple Zero-Resource Syllable Tokenization for Spoken Language Modeling
arXiv:2602.15537v1 Announce Type: new Abstract: Pure speech language models aim to learn language directly from raw audio without textual resources. A key challenge is that discrete tokens from self-supervised speech encoders result in excessively long sequences, motivating recent work on syllable-like units. However, methods like Sylber and SyllableLM rely on intricate multi-stage training pipelines. We propose ZeroSyl, a simple training-free method to extract syllable boundaries and embeddings directly from a frozen WavLM model. Using L2 norms of features in WavLM's intermediate layers, ZeroSyl achieves competitive syllable segmentation performance. The resulting segments are mean-pooled, discretized using K-means, and used to train a language model. ZeroSyl outperforms prior syllabic tokenizers across lexical, syntactic, and narrative benchmarks. Scaling experiments show that while finer-grained units are beneficial for lexical tasks, our discovered syllabic units exhibit better sc
arXiv:2602.15537v1 Announce Type: new Abstract: Pure speech language models aim to learn language directly from raw audio without textual resources. A key challenge is that discrete tokens from self-supervised speech encoders result in excessively long sequences, motivating recent work on syllable-like units. However, methods like Sylber and SyllableLM rely on intricate multi-stage training pipelines. We propose ZeroSyl, a simple training-free method to extract syllable boundaries and embeddings directly from a frozen WavLM model. Using L2 norms of features in WavLM's intermediate layers, ZeroSyl achieves competitive syllable segmentation performance. The resulting segments are mean-pooled, discretized using K-means, and used to train a language model. ZeroSyl outperforms prior syllabic tokenizers across lexical, syntactic, and narrative benchmarks. Scaling experiments show that while finer-grained units are beneficial for lexical tasks, our discovered syllabic units exhibit better scaling behavior for syntactic modeling.
Executive Summary
The article presents ZeroSyl, a novel method for zero-resource syllable tokenization in spoken language modeling. By leveraging a frozen WavLM model, ZeroSyl achieves competitive syllable segmentation performance without relying on intricate multi-stage training pipelines. The proposed method involves using L2 norms of features in WavLM's intermediate layers to extract syllable boundaries and embeddings. The resulting syllabic units are then used to train a language model, outperforming prior syllabic tokenizers across various benchmarks. The scalability of ZeroSyl is shown to be particularly beneficial for syntactic modeling. This development holds significant promise for the advancement of spoken language processing and potentially broadening the applicability of language models in resource-constrained environments.
Key Points
- ▸ ZeroSyl proposes a simple training-free method for extracting syllable boundaries and embeddings from a frozen WavLM model.
- ▸ The method leverages L2 norms of features in WavLM's intermediate layers to achieve competitive syllable segmentation performance.
- ▸ ZeroSyl outperforms prior syllabic tokenizers across lexical, syntactic, and narrative benchmarks.
Merits
Strength
ZeroSyl's simplicity and training-free approach make it an attractive alternative to more complex methods like Sylber and SyllableLM.
Strength
The proposed method's competitive performance across various benchmarks demonstrates its efficacy in spoken language modeling.
Strength
ZeroSyl's scalability is particularly beneficial for syntactic modeling, offering a significant advantage over prior methods.
Demerits
Limitation
The reliance on a pre-trained WavLM model may limit the applicability of ZeroSyl in scenarios where such models are not readily available or are not well-suited to the task at hand.
Limitation
The method's performance may degrade in scenarios with varying audio quality or noisy speech, requiring further investigation into its robustness.
Expert Commentary
The proposed method of ZeroSyl marks a notable advancement in spoken language modeling, particularly in the realm of syllable tokenization. By leveraging a frozen WavLM model, ZeroSyl achieves competitive performance without the need for intricate multi-stage training pipelines. This simplicity and efficacy make it an attractive solution for various applications, including voice assistants and speech recognition systems. Furthermore, the scalability of ZeroSyl for syntactic modeling holds significant promise for broadening the applicability of language models in resource-constrained environments. However, it is essential to investigate the method's robustness in scenarios with varying audio quality or noisy speech to ensure its widespread adoption.
Recommendations
- ✓ Future research should focus on evaluating ZeroSyl's performance in scenarios with varying audio quality or noisy speech to ensure its robustness.
- ✓ Investigating the applicability of ZeroSyl to other spoken language modeling tasks, such as language translation or sentiment analysis, could further demonstrate its potential and versatility.
