MPSA-DenseNet: Advanced Deep Learning-Based Method for English Accent Classification

Table of Contents

• 1 Gabatarwa
• 2 Hanyoyi da Kayayyaki
  • 2.1 Tattara da Tsara Bayanai
  • 2.2 Tsarin Ƙirar Ƙira
    • 2.2.1 Multi-task DenseNet
    • 2.2.2 PSA-DenseNet
    • 2.2.3 MPSA-DenseNet
  • 2.3 Fasahar da Fasaha
• 3 Sakamako da Bincike
• 4 Discussion and Future Directions
• 5 References

1 Gabatarwa

Rarraba lafazin ya zama ƙalubale mai mahimmanci a fagen fasahar magana, musamman ga Ingilishi da ke da bambance-bambance na yanki. Wannan maƙala ta gabatar da ƙirar zurfin koyo guda uku na ƙirƙira—Multi-DenseNet, PSA-DenseNet, da MPSA-DenseNet—waɗanda suke haɗa aikin koyo da yawa da tsarin kulawa tare da tsarin DenseNet don haɓaka aikin rarraba lafazin Ingilishi.

2 Hanyoyi da Kayayyaki

2.1 Tattara da Tsara Bayanai

Wannan binciken ya yi amfani da bayanan muryoyin Ingilishi guda shida na yaruka: yankunan masu magana da Ingilishi a asali (Biritaniya, Amurka, Scotland) da yankunan da ba masu magana da Ingilishi a asali ba (China, Germany, India). An canza siginar odiyo zuwa ma'aunin juzu'in mitar Mel (MFCC) ta hanyar daidaitaccen tsari cirewa: $MFCC = DCT(\log(Mel(|STFT(signal)|^2)))$, inda STFT ke wakiltar canjin Fourier na ɗan lokaci, DCT kuma ke wakiltar canjin cosine mai watsewa.

2.2 Tsarin Ƙirar Ƙira

2.2.1 Multi-task DenseNet

Multitask DenseNet yana amfani da tsarin koyon ayyuka da yawa, inda samfurin ya koyi rarrabawar lafazi da ayyukan taimako (kamar tantance jinsin mai magana ko hasashen rukunin shekaru) a lokaci guda. Aikin asara ya haɗa da maƙasudai da yawa: $L_{total} = \alpha L_{accent} + \beta L_{auxiliary}$, inda $\alpha$ da $\beta$ suke ma'aunin nauyi.

2.2.2 PSA-DenseNet

PSA-DenseNet yana haɗa na'urar Polarized Self-Attention (PSA) cikin tsarin DenseNet. Tsarin hankali yana lissafa kamar haka: $Attention(Q,K,V) = softmax(\frac{QK^T}{\sqrt{d_k}})V$, inda Q, K, V suke wakiltar matrices na tambaya, makulli da kima bi da bi, $d_k$ kuma yana nuna girman ma'auni.

2.2.3 MPSA-DenseNet

MPSA-DenseNet yana haɗa koyon ayyuka da yawa da hanyar kulawa ta PSA, yana ƙirƙirar wani tsari mai gauraye, yana amfani da fa'idodin hanyoyin biyu don cimma kyakkyawan aikin rarraben lafazi.

2.3 Fasahar da Fasaha

The model is implemented using the PyTorch framework, with the following main components:

class MPSADenseNet(nn.Module):

3 Sakamako da Bincike

Sakamakon gwaji ya nuna cewa MPSA-DenseNet ya kai mafi girman ingancin rarrabuwa na kashi 94.2, wanda ya fi DenseNet na asali (87.5%) da kuma tsarin EPSA (91.3%) gaba daya. Matrix din rudani ya nuna cewa tsarin ya yi fice musamman akan lafazin Ingilishi na Indiya (96.1%) da Ingilishi na Amurka (95.4%), yayin da ingancin rarrabuwa na Ingilishi na Scotland (92.7%) ya ɗan ragu amma har yana da ban sha'awa.

In-depth Analysis

MPSA-DenseNet model represents a significant advancement in accent classification by effectively integrating multi-task learning with attention mechanisms. This approach aligns with recent trends in speech processing that leverage complementary techniques to enhance performance. Just as CycleGAN (Zhu et al., 2017) revolutionized image-to-image translation by combining cycle consistency with adversarial training, MPSA-DenseNet demonstrates the powerful potential of architectural hybridization in speech domain.

The multitask learning component addresses the fundamental challenge of limited labeled accent data by enabling the model to learn shared representations across related tasks. This approach has proven successful in other domains, such as Google's BERT model (Devlin et al., 2018) using masked language modeling as an auxiliary task. The PSA attention mechanism, inspired by the self-attention principle in Transformer (Vaswani et al., 2017), allows the model to focus on phonetically significant regions in speech signals, similar to how humans perceive accent variations.

Compared to traditional MFCC-based methods documented in INTERSPEECH conferences, deep learning methods demonstrate superior feature learning capabilities. The 94.2% accuracy achieved by MPSA-DenseNet significantly surpasses the 82-87% range typically reported for SVM and HMM-based methods in accent classification literature. This performance improvement is particularly remarkable considering the inclusion of challenging non-native accents (which generally exhibit greater variability than native dialects).

The success of MPSA-DenseNet points to promising directions for future research, including adaptation to low-resource languages and integration with end-to-end speech recognition systems. As noted in recent IEEE/ACM Transactions on Audio, Speech, and Language Processing publications, the combination of attention mechanisms with multitask learning represents a powerful paradigm for addressing complex audio processing challenges.

4 Discussion and Future Directions

Tsarin MPSA-DenseNet yana nuna babban yuwuwar aikace-aikace na ainihi a cikin tsarin gane murya, dandanon koyon harshe, da ilimin harshe na shari'a. Hanyoyin bincike na gaba sun haɗa da:

• Fadada zuwa harsuna da yarukan da ba su da yawan albarkatu
• Daidaita lafazin baki-lokaci a tsarin murya-zuwa-rubutu
• Haɗawa da tsarin Transformer don haɓaka fahimtar mahallin
• Aikace-aikace a cikin tsarin koyon harshe na keɓance
• Haɓaka tsarin gane murya ta atomatik (ASR) mai jure wa tasirin lafazi

5 References

1. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision.
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems.
3. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
4. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition.
5. Song, T., Nguyen, L. T. H., & Ta, T. V. (2023). MPSA-DenseNet: A novel deep learning model for English accent classification. arXiv preprint arXiv:2306.08798.