Leveraging Text Data to Improve Automatic Speech Recognition for the Low-Resource Language Hawaiian

Kernkonzepte

Incorporating a large Hawaiian language model into the Whisper automatic speech recognition foundation model can provide a small but significant improvement in transcription accuracy for Hawaiian audio.

Zusammenfassung

The authors address the challenge of improving Automatic Speech Recognition (ASR) for the low-resource language Hawaiian by incorporating large amounts of independent Hawaiian text data into the Whisper ASR foundation model.
Key highlights:

The authors evaluate the zero-shot transfer performance of different Whisper model sizes on a manually curated Hawaiian ASR test set. The largest Whisper models (large and large-v2) achieve the best baseline performance with word error rates (WERs) around 22%.
To leverage the available Hawaiian text data, the authors train an external Hawaiian language model (LM) on ~1.5M words of modern Hawaiian text. They then use this LM to rescore the Whisper outputs.
Rescoring the large-v2 Whisper model with the Hawaiian LM provides a small but significant improvement, reducing the WER to around 20%.
The authors analyze example ASR predictions and identify challenges the model faces, such as accurately capturing Hawaiian phonemes like glottal stops and long vowels that differ from English.
The authors discuss ways to further improve Hawaiian ASR by leveraging additional unlabeled text and audio data through techniques like self-supervised learning and pseudo-labeling.

Statistiken

The training set for the Hawaiian LM consists of 45,769 lines, 1,547,831 words, or 7,573,569 characters.
The validation set for the Hawaiian LM consists of 888 lines, 26,607 words, or 129,487 characters.
The ASR test set consists of 57 audio-text pairs, comprising 1,120 words and a total audio duration of 7 minutes and 35.336 seconds.

Zitate

"Mai ho'om¯
auna i ka 'ai: Language Models Improve Automatic Speech Recognition in Hawaiian"
"Leave nothing to waste" or "Use all the data you have"

Wichtige Erkenntnisse aus

Mai Ho'omāuna i ka 'Ai

by Kaavya Chapa... um arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03073.pdf

Tiefere Fragen

How can the authors leverage additional unlabeled Hawaiian text and audio data to further improve the ASR performance?

To enhance ASR performance, the authors can employ self-supervised learning techniques on the unlabeled Hawaiian text and audio data. Self-supervised learning methods, such as contrastive predictive coding or masked language modeling, can help the model learn useful representations from the data without requiring explicit labels. By pre-training the ASR model on a large corpus of unlabeled data, it can capture the underlying patterns and structures of the Hawaiian language, leading to improved transcription accuracy when fine-tuned on labeled data. Additionally, pseudo-labeling can be utilized to generate pseudo-labels for the unlabeled data based on the model's predictions, enabling semi-supervised learning and further improving performance.

What other techniques, beyond language model rescoring, could be explored to better capture the unique phonological features of Hawaiian that differ from English?

In addition to language model rescoring, the authors could explore techniques such as phoneme-level modeling and acoustic modeling tailored specifically to the phonological characteristics of Hawaiian. Phoneme-level modeling involves training the ASR model to recognize and differentiate between the distinct phonemes of the Hawaiian language, including glottal stops and long vowels. Acoustic modeling can focus on capturing the acoustic properties of Hawaiian speech, which may differ from English due to variations in vowel length and glottalization. By incorporating these specialized models into the ASR system, the model can better handle the unique phonological features of Hawaiian and improve transcription accuracy.

How might the authors' approach to leveraging text data for low-resource ASR be applicable to other endangered or minority languages around the world?

The authors' approach of leveraging text data for low-resource ASR can be highly relevant and applicable to other endangered or minority languages worldwide. Many endangered languages have limited labeled audio data but abundant text resources, similar to the Hawaiian language. By training language models on large text corpora in these languages, researchers can improve ASR systems' performance through language model rescoring. Additionally, techniques like self-supervised learning and pseudo-labeling can be utilized to make the most of unlabeled text and audio data, enabling semi-supervised learning and enhancing transcription accuracy. This approach can be a valuable strategy for preserving and revitalizing endangered languages by making ASR technology more accessible and effective for these linguistic communities.