Overview of FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
The paper introduces FLEURS, an innovative benchmark dataset aimed at evaluating universal representations of speech through few-shot learning across a comprehensive array of languages. The dataset is a significant extension of the machine translation benchmark FLoRes-101 and comprises n-way parallel speech data in 102 languages. This benchmark is important for advancing multilingual speech technology, especially in under-resourced languages.
Key Features and Contributions
- Dataset Composition: FLEURS includes approximately 12 hours of labeled speech data per language. The data consists of natural spoken utterances derived from Wikipedia sentences, which are n-way parallel, meaning each sentence is provided in multiple languages by native speakers. This structure ensures that the dataset provides robust support for evaluating a wide array of speech processing tasks.
- Task Variety: The dataset is designed for a range of tasks including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), speech translation, and cross-modal retrieval tasks (such as Speech-to-Text and Text-to-Speech Retrieval). The breadth of tasks underscores the dataset's versatility and its potential in evaluating speech models in low-resource contexts.
- Evaluation and Baseline Models: Researchers provided baseline results utilizing state-of-the-art multilingual pretrained models such as mSLAM and w2v-BERT, covering ASR, Speech LangID, and retrieval. They emphasize the importance of pretraining on diverse data sources to improve few-shot learning across our extensive language set.
- Diversity in Language Coverage: Covering 102 languages from 17 family groups with 27 writing systems, FLEURS is notable for its linguistic and orthographic diversity. This linguistic breadth, encompassing languages from various geographic regions, presents unique challenges and opportunities for speech technology development.
- Seen vs. Unseen Languages: Within the dataset, languages are categorized as "seen" or "unseen" based on their occurrence in pretraining datasets used by the baseline models. This classification allows researchers to assess the generalizability of models to completely novel languages, providing a richer understanding of model performance in real-world conditions.
Numerical Results and Observations
- Character Error Rate (CER) in ASR: The baseline results exhibit differential performance across language groups, with better ASR performance observed in Western Eastern European languages compared to Sub-Saharan African and South Asian languages. This discrepancy signals the need for more balanced pretraining datasets across different linguistic groups.
- Speech LangID: The accuracy rates vary widely among language groups, reflecting the complexity of distinguishing languages within regions characterized by high linguistic diversity. CJK languages offer the highest LangID accuracy, followed by Western and Eastern European languages, highlighting the performance gap for underrepresented language groups.
- Cross-modal Retrieval: The Speech-to-Text and Text-to-Speech retrieval P@1 scores are particularly low for CJK languages, pinpointing the need for improved tokenization strategies and greater integration of multimodal data in pretraining pipelines.
Implications and Future Directions
FLEURS embodies an essential step forward in democratizing speech technology, especially for underrepresented and low-resource languages. By facilitating evaluation across a wide linguistic spectrum, FLEURS can significantly impact the development of inclusive and accessible speech technologies. Furthermore, the dataset sets a high bar for future speech models, particularly in the context of few-shot and multilingual learning.
Future research directions include refining LLMs to improve generalization in unseen languages and developing better tokenization methods to accommodate diverse writing systems, particularly for complex scripts. Enhanced pretraining strategies that consider both speech and text modalities more holistically will also advance the field.
In summary, FLEURS stands as a critical resource for fostering advancements in multilingual speech representation learning, encouraging the development of technologies that are more inclusive of global linguistic diversity.