AccentFold: Enhancing Accent Recognition - Conclusion, Limitations, and References

Table of Links

Abstract and 1 Introduction

2 Related Work

3 AccentFold

4 What information does AccentFold capture?

5 Empirical study of AccentFold

6 Conclusion, Limitations, and References

6 Conclusion

In conclusion, our research addresses the challenge of speech recognition for African accented speech by exploring the linguistic relationships of accent embeddings obtained through AccentFold. Our exploratory analysis of AccentFold provides insights into the spatial relationships between accents and reveals that accent embeddings group together based on geographic and language family similarities, capturing phonological, and morphological regularities based on language families. Furthermore, we reveal, in Section 4.1, two interesting relationships in some African accents that have been uncharacterized by the Ethnologue. Our experimental setup demonstrates the practicality of AccentFold as an accent subset selection method for adapting ASR models to targeted accents. With a WER improvement of 3.5%, AccentFold presents a promising approach for improving ASR performance on accented speech, particularly in the context of African accents, where data scarcity and budget constraints pose significant challenges. Our research paves the way for a deeper understanding of accent diversity and linguistic affiliations, thereby opening new avenues for leveraging linguistic knowledge in adapting ASR systems to target accents.

Limitations

One limitation of our study is the utilization of a single pre-trained model for fine-tuning in our experiments. While the chosen model demonstrated promising performance, this approach may the generalizability and robustness of our findings. Incorporating multiple pre-trained models with varying architectures and configurations would provide a more comprehensive evaluation of the ASR system’s performance.

Furthermore, our study primarily focuses on improving the ASR performance for English with a focus on African accents. Consequently, the findings and outcomes may not be directly transferable to languages outside of the African continent. The characteristics and phonetic variations inherent in non-African accents require tailored approaches to improve ASR systems in different linguistic contexts. Future studies should expand the scope to encompass a broader range of languages and accents to enhance the generalizability of our method beyond African languages.

t-SNE, a stochastic dimensionality reduction algorithm, is highly effective in preserving local structures and representing non-linear relationships in data (Roca et al., 2023). Hence it serves as a versatile and robust tool for visualizing highdimensional data and has been used extensively in myriad domains: for example in the medical domain it is used in visualizing and understanding single-cell sequencing data (Becht et al., 2019; Kobak and Berens, 2019). However, it should be noted that t-SNE is primarily used for data visualization purposes. Therefore, the insights discussed in Section 4 are solely derived from the exploratory analysis conducted using AccentFold and are not based on the inherent capabilities of t-SNE itself. The results obtained from t-SNE analysis should be interpreted with caution, as previous research has demonstrated (Roca et al., 2023; Becht et al., 2018).

Ethics Statement

We use AfriSpeech-200 dataset (Olatunji et al., 2023b) in this paper to run our experiments. This dataset is released under CC BY-NC-SA 4.0. As we use it only for research purpose or not for any commercial purpose, we do not go against the license. We do not foresee any harmful effects or usages of the methodology proposed or the models. We release all the artefacts created as part of this work under CC BY-NC-SA 4.0.

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