Textless Dependency Parsing by Labeled Sequence Prediction (2407.10118v1)

Abstract: Traditional spoken language processing involves cascading an automatic speech recognition (ASR) system into text processing models. In contrast, "textless" methods process speech representations without ASR systems, enabling the direct use of acoustic speech features. Although their effectiveness is shown in capturing acoustic features, it is unclear in capturing lexical knowledge. This paper proposes a textless method for dependency parsing, examining its effectiveness and limitations. Our proposed method predicts a dependency tree from a speech signal without transcribing, representing the tree as a labeled sequence. scading method outperforms the textless method in overall parsing accuracy, the latter excels in instances with important acoustic features. Our findings highlight the importance of fusing word-level representations and sentence-level prosody for enhanced parsing performance. The code and models are made publicly available: https://github.com/mynlp/SpeechParser.

• The paper introduces a textless dependency parsing approach that predicts labeled sequences directly from speech, eliminating the need for transcription.
• It leverages wav2vec2-based feature extraction with CTC loss to capture prosodic nuances, thereby enhancing syntactic disambiguation.
• Comparative analysis shows the textless method offers faster training and better prosody handling, despite lower performance in long-distance dependency accuracy.

Textless Dependency Parsing by Labeled Sequence Prediction

The paper "Textless Dependency Parsing by Labeled Sequence Prediction" introduces an innovative approach to dependency parsing directly from speech signals without requiring intermediate text transcriptions. Traditionally, spoken language processing comprises cascading an Automatic Speech Recognition (ASR) system with text-based models, which may lead to the propagation of ASR errors and a loss of critical acoustic features like prosody. The paper contrasts this method with a newly proposed "textless" approach, offering empirical insight into the advantages and limitations of both methods.

Methodology

The paper explores a method that predicts dependency trees directly from speech signals, representing these trees as labeled sequences. This approach can be seen as a textless sequence-to-sequence problem, enabling the direct utilization of acoustic features without transcribing speech to text first. The paper utilizes a wav2vec2-based feature extraction process combined with Connectionist Temporal Classification (CTC) loss to train the model in generating labeled sequences akin to dependency parses.

Comparative Analysis with Cascading Methods

The textless method was rigorously compared against an established cascading method named Wav2tree, which first transcribes speech into text and subsequently performs dependency parsing. Experiments were conducted on two datasets: Orféo Treebank for French and the Switchboard Corpus for English. Across various metrics—Word Error Rate (WER), Character Error Rate (CER), Unlabeled Attachment Score (UAS), and Labeled Attachment Score (LAS)—the cascading method generally outperformed the textless method. However, it is noteworthy that the textless method excelled in cases where prosodic features played a crucial role in disambiguating syntactic structure, such as identifying the main verb of a sentence.

Implications and Key Findings

1. Accuracy of Long-Distance Dependencies: The text-based cascading method demonstrated superior parsing accuracy, particularly in handling long-distance dependencies. This suggests that segmenting speech at word boundaries is critical for maintaining parsing performance.
2. Pronunciational Nuances: In the textless approach, instances where prosodic features (such as stress and pitch) were significant saw improved accuracy. This highlights the potential of incorporating prosodic information directly into the parsing process, which is often neglected in traditional ASR transcription.
3. Efficiency: The textless model, due to fewer parameters and lack of a dedicated parsing network, demonstrated faster training times compared to the cascading approach. This efficiency benefit is an essential consideration for practical applications.

Future Directions

The paper opens multiple avenues for future research. Integrating sentence-level prosody with word-level representations could significantly enhance parsing accuracy. Furthermore, exploring alternative architectures that relax the conditional independence assumption inherent in CTC—such as attention mechanisms or intermediate CTC architectures—could potentially improve performance. Lastly, developing evaluation datasets specifically targeted at syntactic disambiguation through audio features would provide deeper insights into the interplay of prosody and syntax.

Conclusion

The paper provides a comprehensive evaluation of textless dependency parsing and presents a compelling case for the preservation and utilization of prosodic features in syntactic parsing from speech. The findings underscore the nuanced benefits of prosodic features and suggest a balanced approach of integrating word-level and sentence-level audio features for robust spoken language understanding. As this research progresses, it will contribute significantly to advancements in natural language processing, particularly in enhancing the accuracy and efficiency of speech-to-meaning systems.