Improving Multilingual Automatic Speech Recognition for Low-Resource Languages using Weighted Cross-Entropy and Data Augmentation

Conceptos Básicos

Applying language-weighted dynamic cross-entropy and data augmentation techniques to effectively incorporate low-resource languages into pre-trained multilingual automatic speech recognition models without degrading performance on high-resource languages.

Resumen

This paper addresses the challenge of integrating low-resource languages into multilingual automatic speech recognition (ASR) systems. The authors introduce a novel approach that combines weighted cross-entropy and data augmentation techniques to facilitate the integration of low-resource languages into pre-trained multilingual ASR models within the context of continual multilingual learning.

The key highlights and insights are:

1. The authors propose two strategies for applying weighted cross-entropy: linear progressive weighting and dynamic weight adaptation based on the ratio of average losses between low-resource and high-resource languages.

2. They apply data augmentation techniques (time stretching, gain adjustment, pitch shifting, and noise addition) only to the low-resource language to increase the model's exposure to diverse speech variations.

3. Experiments are conducted using the Whisper multilingual ASR model, fine-tuning it on five high-resource languages (Spanish, Portuguese, French, German, English) and one low-resource language (Galician).

4. The results show a remarkable 6.69% word error rate (WER) reduction for the low-resource language Galician compared to the fine-tuned model without weighted cross-entropy, and a 48.86% WER reduction compared to the original Whisper model.

5. On average, the proposed approach achieves a 3.29% WER relative reduction across the six languages under study when compared to the simple fine-tuned model, with no degradation for the high-resource languages.

6. The combination of weighted cross-entropy with dynamic weight adaptation and data augmentation for the low-resource language emerges as the most effective strategy, improving performance for the target language while leveraging cross-lingual transfer properties to enhance recognition in the other languages.

Estadísticas

"The results show a remarkable 6.69% word error rate (WER) reduction for the low-resource language Galician compared to the fine-tuned model without weighted cross-entropy, and a 48.86% WER reduction compared to the original Whisper model."
"On average, the proposed approach achieves a 3.29% WER relative reduction across the six languages under study when compared to the simple fine-tuned model, with no degradation for the high-resource languages."

Citas

"The combination of weighted cross-entropy with dynamically adapted weights, guided by losses at each training step, combined with augmented data (WS-FT-DA-WCEGL+), emerges as the most efficient strategy for achieving improved overall recognition results."
"This approach not only significantly improves results for low-resource languages, but also leverages cross-lingual transfer properties to improve recognition in four of the high-resource languages, proving to be robust and effective across different language contexts."

Ideas clave extraídas de

Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech Recognition

by Andr... a las arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16954.pdf

Consultas más profundas

How can the proposed techniques be extended to incorporate more than one low-resource language into the multilingual ASR model?

To extend the proposed techniques for incorporating multiple low-resource languages into the multilingual automatic speech recognition (ASR) model, several strategies can be employed. First, the weighted cross-entropy approach can be adapted to assign different weights to each low-resource language based on their respective data availability and linguistic characteristics. This would involve creating a weight vector that reflects the importance of each low-resource language during training, allowing the model to prioritize learning from these languages without compromising the performance of high-resource languages.
Additionally, the dynamic weight adaptation method can be modified to calculate weights for multiple low-resource languages by considering the average losses across all low-resource languages in the batch. This would ensure that the model dynamically adjusts its focus based on the performance of each low-resource language, promoting a more balanced learning process.
Furthermore, data augmentation techniques can be tailored to each low-resource language, ensuring that the model is exposed to a diverse range of speech patterns and variations specific to each language. Techniques such as time-stretching, pitch shifting, and noise addition can be applied individually to each low-resource language dataset, effectively increasing the training samples and enhancing the model's robustness.
Finally, a multi-task learning framework could be implemented, where the model is trained simultaneously on multiple low-resource languages, allowing it to leverage shared linguistic features and improve overall performance across all languages involved.

What other data augmentation strategies could be explored to further enhance the performance of low-resource languages in the multilingual setting?

In addition to the data augmentation techniques already employed, several other strategies could be explored to enhance the performance of low-resource languages in a multilingual ASR setting.

SpecAugment: This technique, which involves masking parts of the spectrogram, can be particularly effective in improving the model's robustness to variations in speech. By randomly masking portions of the audio input, the model learns to generalize better and can handle real-world noise and distortions.

Voice Conversion: This method involves transforming the voice characteristics of a speaker to mimic those of another speaker. By generating synthetic audio samples that represent different speakers, the model can learn to recognize speech patterns across various vocal characteristics, which is especially beneficial for low-resource languages with limited speaker diversity.

Synthetic Data Generation: Leveraging text-to-speech (TTS) systems to create synthetic audio data can significantly increase the amount of training data available for low-resource languages. By generating high-quality speech from text, the model can be trained on a larger and more diverse dataset, improving its performance.

Noise Injection: Beyond Gaussian noise, incorporating various types of background noise (e.g., urban sounds, crowd noise, or music) can help the model become more resilient to different acoustic environments. This is particularly important for low-resource languages that may be used in diverse settings.

Language-Specific Augmentation: Tailoring augmentation techniques to the phonetic and prosodic characteristics of each low-resource language can yield better results. For instance, adjusting the pitch and tempo based on the linguistic features of the target language can enhance the model's ability to recognize speech accurately.

By exploring these additional data augmentation strategies, researchers can further improve the performance of multilingual ASR systems for low-resource languages, ultimately leading to more equitable language technology.

What are the potential implications of this work for the broader field of multilingual natural language processing beyond speech recognition?

The implications of this work extend significantly beyond the realm of multilingual automatic speech recognition (ASR) and can influence various aspects of multilingual natural language processing (NLP).

Improved Language Inclusivity: By developing techniques that enhance the performance of low-resource languages, this research contributes to the broader goal of digital language equality. It paves the way for more inclusive language technologies that can cater to underrepresented languages, thereby promoting linguistic diversity in digital spaces.

Cross-Lingual Transfer Learning: The methodologies employed, such as weighted cross-entropy and dynamic weight adaptation, can be applied to other NLP tasks, such as machine translation, sentiment analysis, and text classification. These techniques can help models learn from high-resource languages and transfer that knowledge to low-resource languages, improving performance across various NLP applications.

Data Efficiency: The focus on data augmentation and continual learning strategies can lead to more data-efficient models that require less labeled data to achieve high performance. This is particularly beneficial in NLP, where labeled data can be scarce for many languages, allowing for the development of robust models with minimal resources.

Enhanced Model Robustness: The integration of diverse data augmentation techniques can improve the robustness of NLP models against noise and variations in input data. This robustness is crucial for real-world applications, where language use can vary significantly based on context, speaker, and environment.

Framework for Future Research: The findings and methodologies presented in this work can serve as a foundation for future research in multilingual NLP. By establishing effective strategies for low-resource languages, researchers can explore new avenues for enhancing language technologies, ultimately leading to advancements in the field.

In summary, the work on integrating low-resource languages into multilingual ASR systems has far-reaching implications for the broader field of multilingual NLP, fostering inclusivity, efficiency, and robustness in language technologies.