Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition (1803.04567v2)

Abstract: Dialect identification (DID) is a special case of general language identification (LID), but a more challenging problem due to the linguistic similarity between dialects. In this paper, we propose an end-to-end DID system and a Siamese neural network to extract language embeddings. We use both acoustic and linguistic features for the DID task on the Arabic dialectal speech dataset: Multi-Genre Broadcast 3 (MGB-3). The end-to-end DID system was trained using three kinds of acoustic features: Mel-Frequency Cepstral Coefficients (MFCCs), log Mel-scale Filter Bank energies (FBANK) and spectrogram energies. We also investigated a dataset augmentation approach to achieve robust performance with limited data resources. Our linguistic feature research focused on learning similarities and dissimilarities between dialects using the Siamese network, so that we can reduce feature dimensionality as well as improve DID performance. The best system using a single feature set achieves 73% accuracy, while a fusion system using multiple features yields 78% on the MGB-3 dialect test set consisting of 5 dialects. The experimental results indicate that FBANK features achieve slightly better results than MFCCs. Dataset augmentation via speed perturbation appears to add significant robustness to the system. Although the Siamese network with language embeddings did not achieve as good a result as the end-to-end DID system, the two approaches had good synergy when combined together in a fused system.

• The paper introduces an end-to-end dialect identification system combining Convolutional Neural Networks for acoustic features with language embeddings from a Siamese neural network for linguistic features.
• The fusion system achieved 78% accuracy on the MGB-3 Arabic dataset, demonstrating the effectiveness of combining acoustic and linguistic approaches.
• Data augmentation, particularly speed perturbation and internal utterance segmentation, significantly improved system robustness and reduced reliance on extensive external datasets.

Summary of "Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition"

This paper by Suwon Shon, Ahmed Ali, and James Glass discusses advancements in the field of dialect identification (DID) through the development of systems integrating convolutional neural networks (CNNs) and language embeddings. With a focus on the Arabic dialectal speech dataset, Multi-Genre Broadcast 3 (MGB-3), the authors present a sophisticated end-to-end approach for distinguishing between similar language dialects using both acoustic and linguistic features.

Dialect identification poses unique challenges due to the high linguistic similarity among dialects compared to more general language identification tasks. The paper introduces two divergent methods to enhance DID performance. The first is an end-to-end system employing CNNs with global average pooling layers, trained using acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs), log Mel-scale Filter Bank energies (FBANK), and spectrogram energies. The second approach leverages linguistic features through a Siamese neural network designed to extract language embeddings, thereby learning the similarities and dissimilarities between dialects.

A notable achievement outlined in this paper is a 78% accuracy rate on the MGB-3 test dataset when employing a fusion system combining these techniques. The paper underscores the importance of acoustic feature selection, with FBANK features showing slightly superior performance compared to MFCCs. Furthermore, spectrogram-based features proved viable when sufficient training data was available. The incorporation of data augmentation techniques such as speed perturbation significantly boosted system robustness, highlighting the necessity of diversifying limited data resources.

The authors make a strong case for end-to-end systems trained with internal dataset augmentation methods, notably random utterance segmentation and data perturbation. These methods substantially reduce the need for extensive external datasets, promoting versatility and efficiency. Additionally, the paper reports that language embedding-based systems offer insights into feature dimensionality reduction while enhancing DID performance, showcasing good synergy with acoustic feature-based models.

This research has several implications for future developments in AI and dialect recognition. Practically, the findings suggest that CNNs and language embeddings can be effectively utilized to tackle dialect complexity and similarity issues, potentially extending their application to broader language identification tasks. Theoretically, the integration of Siamese networks may pave the way for robust classification systems capable of operating on high-dimensional datasets while maintaining accuracy.

In conclusion, this paper contributes valuable methodologies and results to the field of dialect recognition competition. It prompts further exploration into efficient feature integration and dataset augmentation, facilitating the development of advanced AI systems responsive to linguistic subtlety across dialects.