Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programming
Abstract
We present a method for accurate multilingual word-level forced alignment, consisting of an alignment encoder and a learned alignment decoder. The encoder integrates two representations: one from the Massively Multilingual Speech (MMS) model and another from a self-supervised phoneme boundary detector (UnSupSeg). It learns to fuse them and to estimate word-boundary probabilities over long temporal contexts. The alignment decoder is a learned dynamic programming that combines encoder outputs with segmental features over the MMS and UnSupSeg representations to infer final word boundaries. Trained iteratively on TIMIT and Buckeye, the proposed approach outperforms Montreal Forced Aligner (MFA) and MMS-based alignment on both datasets. On unseen languages (Dutch, German, and Hebrew), the proposed model achieves performance consistently better than or on par with existing alignment approaches, indicating its potential to scale to 1100+ languages supported by MMS without further training.
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 paper presents a significant advancement in multilingual word-level forced alignment through an innovative integration of self-supervised representations. The methodology and experimental results demonstrate its potential to outperform existing systems, highlighting its relevance and impact in the field of speech processing.
Methodology
The paper introduces a novel approach to multilingual word-level forced alignment by integrating representations from the Massively Multilingual Speech (MMS) model and a self-supervised phoneme boundary detector (UnSupSeg). The architecture comprises an alignment encoder and a learned dynamic programming decoder, which effectively fuses the two representations to improve word boundary estimation. The use of focal loss to address class imbalance in boundary detection is a thoughtful choice, enhancing the model's robustness. The iterative training process and the selection of various encoder architectures (VGG, Transformer, Conformer) demonstrate a comprehensive methodology. However, the paper could benefit from clearer explanations of the mathematical formulations and the dynamic programming approach.
Results & evaluation
The experiments are well-structured, utilizing established datasets (TIMIT, Buckeye) and extending evaluations to unseen languages (Dutch, German, Hebrew). The results indicate that the proposed method outperforms existing systems, including the Montreal Forced Aligner (MFA), across multiple datasets and evaluation metrics. The inclusion of performance comparisons at various tolerance thresholds adds depth to the experimental evaluation. However, the paper lacks detailed statistical significance tests for the reported results, which would strengthen the claims of superiority.
Reproducibility
The authors mention that code and pretrained checkpoints will be released upon acceptance, which is a positive aspect for reproducibility. However, the paper does not provide sufficient details on the training procedures, hyperparameter settings, or the specific configurations of the models used in the experiments. More comprehensive documentation would aid in replicating the results.
Limitations
One limitation is the reliance on pre-trained models, which may introduce biases inherent in those models. The performance on Dutch was noted to be lower than on other languages, suggesting potential challenges in generalization. Additionally, the model's performance on languages not supported by MMS remains untested, which could limit its applicability in diverse linguistic contexts.
Broader impact
The proposed method has significant implications for speech technology, particularly in enhancing the accuracy of automatic speech recognition systems across multiple languages. Its ability to scale to over 1,100 languages without further training opens avenues for broader accessibility and usability in multilingual contexts. This could facilitate advancements in linguistic research and improve user experiences in global applications. The paper presents a significant advancement in multilingual word-level forced alignment through an innovative integration of self-supervised representations. The methodology and experimental results demonstrate its potential to outperform existing systems, highlighting its relevance and impact in the field of speech processing.
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