betekintés - Machine Learning - # Uncertainty-Driven Data Selection for Robust African-Accented ASR

Improving African-Accented Speech Recognition: Leveraging Epistemic Uncertainty for Cost-Effective and Generalizable ASR Models

Q: How can the proposed approach be further extended to handle code-switching and multilingual scenarios common in African speech data?

In order to extend the proposed approach to handle code-switching and multilingual scenarios prevalent in African speech data, several key considerations need to be taken into account: Data Collection and Annotation: Collecting and annotating a diverse dataset that includes code-switching and multilingual speech samples is crucial. This dataset should cover a wide range of languages, accents, and dialects commonly found in African regions. Model Training: The ASR models need to be trained on this diverse dataset to effectively recognize and transcribe code-switching and multilingual speech. Fine-tuning the models with a focus on these specific linguistic phenomena is essential. Epistemic Uncertainty for Code-Switching: Incorporating epistemic uncertainty-based data selection for code-switching scenarios can help the model adapt to the variability and complexity of language mixing. By identifying and focusing on uncertain samples in code-switched speech, the model can improve its performance in handling such scenarios. Language Embeddings: Utilizing language embeddings or language-specific features can enhance the model's ability to distinguish between different languages and dialects within the same utterance, facilitating accurate transcription in multilingual contexts. Continuous Evaluation and Improvement: Regular evaluation of the model's performance on code-switching and multilingual data is essential. Feedback loops should be established to continuously improve the model's capabilities in handling these linguistic variations. By incorporating these strategies, the proposed approach can be extended to effectively handle code-switching and multilingual scenarios in African speech data, enabling more robust and accurate automatic speech recognition systems.

Q: How can the insights from this work on leveraging epistemic uncertainty be applied to other low-resource language domains beyond speech recognition, such as machine translation or natural language understanding?

The insights gained from leveraging epistemic uncertainty in the context of African-accented speech recognition can be applied to other low-resource language domains, such as machine translation and natural language understanding, in the following ways: Data Selection Strategies: Similar to speech recognition, epistemic uncertainty-driven data selection can be employed in machine translation and natural language understanding tasks. By focusing on uncertain or challenging data points, models can be trained more effectively on low-resource languages. Model Adaptation: Iterative model adaptation processes that incorporate epistemic uncertainty can be utilized in machine translation and natural language understanding models. This approach can help improve model performance and generalization in low-resource language settings. Domain Adaptation: Epistemic uncertainty can be leveraged for domain adaptation in machine translation and natural language understanding. By identifying and addressing uncertainties specific to different domains or linguistic variations, models can be tailored to perform better in diverse contexts. Ethical Considerations: Considering the ethical implications of deploying AI systems in low-resource language domains is crucial. Ensuring fairness, transparency, and accountability in machine translation and natural language understanding applications is essential to mitigate potential biases and risks. By applying the insights from leveraging epistemic uncertainty to these domains, researchers and practitioners can enhance the effectiveness and applicability of AI technologies in addressing the challenges of low-resource languages in diverse linguistic contexts.

Alapfogalmak

Developing cost-efficient, robust, and linguistically diverse automatic speech recognition systems for African accents by leveraging epistemic uncertainty-based data selection.

Kivonat

The paper presents an approach to building cost-efficient, robust, and linguistically diverse automatic speech recognition (ASR) systems for African-accented speech. The key insights are:

The authors propose an iterative model adaptation process that uses epistemic uncertainty-based data selection to reduce the required amount of labeled data while outperforming several high-performing ASR models.
The approach improves out-of-distribution generalization for very low-resource accents, demonstrating its viability for building generalizable ASR models in the context of accented African clinical ASR, where training datasets are predominantly scarce.
The authors investigate trends in domain selection (clinical, general, and both) across adaptation rounds, finding that the most uncertain samples from linguistically rich and diverse accents provide the best learning signal for the model.
The authors establish strong baselines for the nascent field of African clinical ASR, providing a foundation for further exploration in this research direction.
The approach is shown to be effective across different ASR model architectures and datasets, demonstrating its model and dataset agnostic nature.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

The use of speech recognition led to a 19-92% decrease in average documentation time, 50.3-100% decrease in turnaround time, and 17% improvement in documentation quality.
There is a shortage of skilled health personnel in many African countries, with a 1.55 health worker per 1000 persons ratio, lower than the WHO-recommended 4.45 health workers per 1000 persons.
The AfriSpeech-200 dataset used in the study contains 200 hours of Pan-African accented English speech, representing 13 Anglophone countries across sub-Saharan Africa and the US.

Idézetek

"Clinical automatic speech recognition (ASR) is an active area of research (Kodish-Wachs et al., 2018; Finley et al., 2018; Zapata and Kirkedal, 2015)."
"Several studies (Blackley et al., 2019; Goss et al., 2019; Blackley et al., 2020; Ahlgrim et al., 2016; Vogel et al., 2015) showed that the use of speech recognition led to a 19-92% decrease in average documentation time, 50.3-100% decrease in turnaround time, and 17% improvement in documentation quality."
"In the African context where the patient burden is high (Oleribe et al., 2019; Naicker et al., 2009; Nkomazana et al., 2015) and staffing is inadequate (who; Ahmat et al., 2022; Naicker et al., 2010; Nkomazana et al., 2015; Kinfu et al., 2009), clinical ASR systems have great potential to reduce daily documentation burden."

Főbb Kivonatok

Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models

by Bonaventure ... : arxiv.org 05-07-2024

https://arxiv.org/pdf/2306.02105.pdf

Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models

Mélyebb kérdések

How can the proposed approach be further extended to handle code-switching and multilingual scenarios common in African speech data?

In order to extend the proposed approach to handle code-switching and multilingual scenarios prevalent in African speech data, several key considerations need to be taken into account:

Data Collection and Annotation: Collecting and annotating a diverse dataset that includes code-switching and multilingual speech samples is crucial. This dataset should cover a wide range of languages, accents, and dialects commonly found in African regions.

Model Training: The ASR models need to be trained on this diverse dataset to effectively recognize and transcribe code-switching and multilingual speech. Fine-tuning the models with a focus on these specific linguistic phenomena is essential.

Epistemic Uncertainty for Code-Switching: Incorporating epistemic uncertainty-based data selection for code-switching scenarios can help the model adapt to the variability and complexity of language mixing. By identifying and focusing on uncertain samples in code-switched speech, the model can improve its performance in handling such scenarios.

Language Embeddings: Utilizing language embeddings or language-specific features can enhance the model's ability to distinguish between different languages and dialects within the same utterance, facilitating accurate transcription in multilingual contexts.

Continuous Evaluation and Improvement: Regular evaluation of the model's performance on code-switching and multilingual data is essential. Feedback loops should be established to continuously improve the model's capabilities in handling these linguistic variations.

By incorporating these strategies, the proposed approach can be extended to effectively handle code-switching and multilingual scenarios in African speech data, enabling more robust and accurate automatic speech recognition systems.

How can the insights from this work on leveraging epistemic uncertainty be applied to other low-resource language domains beyond speech recognition, such as machine translation or natural language understanding?

The insights gained from leveraging epistemic uncertainty in the context of African-accented speech recognition can be applied to other low-resource language domains, such as machine translation and natural language understanding, in the following ways:

Data Selection Strategies: Similar to speech recognition, epistemic uncertainty-driven data selection can be employed in machine translation and natural language understanding tasks. By focusing on uncertain or challenging data points, models can be trained more effectively on low-resource languages.

Model Adaptation: Iterative model adaptation processes that incorporate epistemic uncertainty can be utilized in machine translation and natural language understanding models. This approach can help improve model performance and generalization in low-resource language settings.

Domain Adaptation: Epistemic uncertainty can be leveraged for domain adaptation in machine translation and natural language understanding. By identifying and addressing uncertainties specific to different domains or linguistic variations, models can be tailored to perform better in diverse contexts.

Ethical Considerations: Considering the ethical implications of deploying AI systems in low-resource language domains is crucial. Ensuring fairness, transparency, and accountability in machine translation and natural language understanding applications is essential to mitigate potential biases and risks.

By applying the insights from leveraging epistemic uncertainty to these domains, researchers and practitioners can enhance the effectiveness and applicability of AI technologies in addressing the challenges of low-resource languages in diverse linguistic contexts.