Improving Irish Speech Recognition and Dialect Identification Using a Multi-task Hybrid CTC/Attention Encoder-Decoder Framework

Core Concepts

This paper explores the use of a hybrid CTC/Attention encoder-decoder model trained with Intermediate CTC (InterCTC) for improving Irish speech recognition (ASR) and dialect identification (DID) performance in a multi-task framework.

Abstract

The paper explores the use of a hybrid CTC/Attention encoder-decoder model for jointly modeling Irish speech recognition (ASR) and dialect identification (DID) in a multi-task framework. Key highlights:

The authors investigate the use of Intermediate CTC (InterCTC) to incorporate a DID objective as an auxiliary task during training, in addition to the primary ASR objective.
They systematically explore assigning the InterCTC objectives to different encoder layers and find an optimal configuration that boosts DID accuracy by 10.8% relative to a baseline ECAPA-TDNN model, while also approaching the performance of a strong TDNN-HMM ASR model.
Experiments with different encoder architectures, including Conformer and E-branchformer, are conducted. The E-branchformer Large model with the optimal InterCTC setting and multi-task language model shallow fusion achieves the best overall performance.
The multi-task approach emerges as a promising strategy for improving both ASR and DID performance for the low-resource Irish language, which exhibits significant dialect variation.

Stats

The dataset used consists of 290 hours of training data, 1.7 hours of validation data, and 3.5 hours of test data, with a focus on L1 Irish speakers.
The training data is a combination of recordings from the ABAIR project's M´ıleGl´or and Synthesis corpora, as well as audiobooks and a spontaneous speech corpus.
The validation and test sets are constructed from the M´ıleGl´or corpus, with the addition of a small portion of Ulster dialect data from the audiobook collection.

Quotes

"This multi-task approach emerges as a promising strategy for Irish low-resource ASR and DID."
"The best performing system (row 6) is trained with the DID objective in layer 3 and the multi-task objective in layers 6 and 9. This model achieved the highest DID accuracy in this experiment, a boost of 10.8% relative to the ECAPA-TDNN baseline."

Key Insights Distilled From

Low-resource speech recognition and dialect identification of Irish in a multi-task framework

by Liam... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01293.pdf

Deeper Inquiries

How could the multi-task approach be extended to incorporate additional related tasks, such as language modeling or phoneme recognition, to further improve the overall performance of the system

To extend the multi-task approach for Irish ASR and dialect identification, incorporating additional related tasks like language modeling or phoneme recognition could further enhance the system's performance.

Language Modeling (LM): By integrating LM as an auxiliary task, the model can benefit from improved contextual understanding and better prediction of the next word in the sequence. This can lead to more accurate transcriptions and dialect identification by leveraging language patterns and structures.

Phoneme Recognition: Including phoneme recognition as a task can help in capturing fine-grained acoustic features and improving the model's ability to differentiate between similar sounds in different dialects. This can enhance the overall accuracy of ASR and dialect identification by focusing on phonetic variations specific to Irish dialects.

Joint Training: Training the model jointly on ASR, dialect identification, language modeling, and phoneme recognition tasks can enable shared learning across these related tasks. This shared knowledge can lead to better feature representations, improved generalization, and enhanced performance on all tasks.

By incorporating these additional tasks into the multi-task framework, the model can leverage diverse sources of information and learn more robust representations, ultimately enhancing its performance in low-resource Irish ASR and dialect identification scenarios.

What other techniques, such as data augmentation or transfer learning, could be explored to address the low-resource nature of the Irish language and improve the generalization of the models

To address the low-resource nature of the Irish language and improve model generalization, several techniques can be explored:

Data Augmentation: Augmenting the training data by applying techniques like speed perturbation, spectral augmentation, and noise injection can help in creating a more diverse and robust dataset. This can improve the model's ability to generalize to different dialects and variations in speech.

Transfer Learning: Leveraging pre-trained models on related tasks or languages and fine-tuning them on Irish speech data can help in transferring knowledge and features learned from high-resource languages to the low-resource Irish language. This can boost performance and reduce the need for large amounts of labeled data.

Semi-Supervised Learning: Incorporating unlabeled data along with limited labeled data can be beneficial in training models with limited resources. Techniques like self-training, pseudo-labeling, and consistency regularization can help in utilizing unlabeled data effectively to improve model performance.

Domain Adaptation: Adapting the model to the specific characteristics of the Irish language and dialects by fine-tuning on domain-specific data can enhance its ability to capture dialectal variations and nuances present in the speech data.

By exploring these techniques, the models can overcome the challenges posed by the low-resource nature of the Irish language and achieve better generalization and performance in ASR and dialect identification tasks.

Given the importance of dialect information for Irish ASR, how could the models be adapted to handle the dynamic switching between dialects that may occur in real-world usage scenarios

Handling dynamic switching between dialects in real-world scenarios is crucial for accurate ASR and dialect identification. Here are some ways the models can be adapted:

Dynamic Language Models: Incorporating dynamic language models that can adapt to the detected dialect during runtime can improve the accuracy of transcriptions. These models can switch between different language models based on the identified dialect, ensuring more precise predictions.

Adaptive Acoustic Models: Training adaptive acoustic models that can adjust their parameters based on the detected dialect can enhance the model's ability to recognize dialect-specific acoustic patterns. This adaptability can lead to more accurate and robust performance across different dialects.

Contextual Information: Utilizing contextual information from the dialogue or conversation can help in predicting the likely dialect being spoken. By considering the context of the speech, the model can make more informed decisions when handling dialect variations.

Incremental Learning: Implementing incremental learning techniques that allow the model to continuously update its knowledge as it encounters new dialects can improve its adaptability and performance in handling dynamic dialect switching scenarios.

By incorporating these adaptations, the models can effectively handle the dynamic nature of dialect switching and ensure accurate ASR and dialect identification in real-world usage scenarios.