- The paper introduces an unsupervised framework for aligning speech and text embeddings through domain-adversarial training and a refinement procedure.
- It leverages unsupervised segmentation and clustering methods to process non-parallel audio data, eliminating the need for cross-modal supervision.
- The approach shows promising results in spoken word classification and translation, offering practical benefits for low-resource language applications.
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
This paper introduces a framework for unsupervised cross-modal alignment between speech and text embedding spaces. The approach leverages non-parallel data, eliminating the need for cross-modal supervision, which is significant for many low- or zero-resource languages.
Introduction and Objective
The goal is to achieve cross-modal alignment between embedding spaces learned from speech and text corpora. Inspired by recent advances in unsupervised cross-lingual alignment, this research targets the alignment of these modalities without relying on parallel datasets, enabling applications such as automatic speech recognition (ASR) and speech-to-text translation. These tasks are particularly challenging in languages with limited resources, where traditional supervised learning methods are infeasible.
Learning Speech and Text Embedding Spaces
Speech Embedding with Speech2Vec
Speech embeddings are learned via the Speech2Vec model, which draws inspiration from Word2Vec. It uses an RNN-based encoder-decoder architecture to represent each audio segment as a fixed-dimensional vector encapsulating semantic information. Unlike text, where segments are easily identifiable, speech requires a handling of variable-length sequences, addressed by utilizing sequences of acoustic features.
Unsupervised Segmentation
For true unsupervised learning, the paper suggests using unsupervised segmentation techniques such as BES-GMM, ES-KMeans, and SylSeg to pre-process speech corpora into word-like units. This step is crucial in eliminating forced alignment, which introduces supervision. The clustering approach groups similar embeddings, facilitating the learning of coherent semantic spaces.
Text Embedding via Word2Vec
The text embedding space is trained using the standard Word2Vec method on transcriptions from speech corpora, establishing a semantic representation space without relying on cross-modal supervision.
Cross-Modal Alignment Framework
Domain-Adversarial Training
The alignment between the speech and text spaces is initially performed through domain-adversarial training. By leveraging adversarial methods, the framework learns a mapping that renders the elements of the speech embedding space indistinguishable from those of the text space.
Refinement Procedure
Following adversarial training, a refinement procedure is employed to further improve alignment accuracy. It constructs a synthetic parallel dictionary using mutual nearest neighbors among high-frequency words, solving potential issues in high-dimensional space like hubness, and refines the alignment via conventional linear mappings.
Applications: Spoken Word Classification and Translation
Spoken Word Classification
This task involves recognizing the underlying word of a given audio segment using the trained embedding spaces. The performance is measured by accuracy, showcasing how well the embeddings have been aligned in recognizing spoken words directly.
Spoken Word Translation
Involves translating spoken words (audio segments) as input instead of text, facilitated by the learned alignment between speech and text spaces across different languages. This task assesses precision by measuring the retrieval of correct translations.
Experimental Results
The experiments validate the proposed unsupervised alignment approach.
- Speech Segmentation and Clustering: The segmentation quality significantly affects embedding effectiveness, with unsupervised methods showing varying degrees of success.
- Domain-Adversarial Training: Achieves near parity with supervised methods, providing effective cross-modal transfers.
- Practical Implications: The approach advances capabilities in low-resource language contexts beyond traditional ASR and translation models, highlighting speech and text embeddings' semantic qualities.
Conclusion
The framework effectively aligns speech and text embeddings without supervision, representing a method with broad implications for linguistic diversity. Future work includes enhancing unsupervised speech segmentation and expanding the framework's applications beyond spoken word-focused tasks, aiming at holistic speech processing systems.
The paper indicates promising directions in unsupervised methods, challenging conventional reliance on parallel data and opening avenues for efficient resource allocation in linguistic technology development.