Configurable Multilingual Automatic Speech Recognition Using Speech Summary Representations

Grunnleggende konsepter

This research paper introduces csvMASR, a novel configurable multilingual automatic speech recognition (MASR) model that leverages speech summary vector representations and adapter modules to achieve improved performance and configurability compared to existing MASR models.

Sammendrag

• Bibliographic Information: Zhu, H., Fung, I., Zhu, Y., & Samarakoon, F. (2024). Configurable Multilingual ASR with Speech Summary Representations. arXiv preprint arXiv:2410.04478v1.

• Research Objective: This paper proposes a novel configurable MASR model called csvMASR (Configurable MASR model with Summary Vector) to address the challenges of language confusion and bias towards data-rich languages in existing MASR models.

• Methodology: The csvMASR model utilizes a hybrid CTC-Attention architecture with a Conformer encoder and a Transformer decoder. The key innovations include:

  • Incorporating parameter-efficient adapter modules into the model architecture to enhance language-specific feature learning.
  • Introducing speech summary vector representations, inspired by conversational summary representations in speech diarization, to determine language-specific weights at the utterance level, improving language classification accuracy.
  • Adding an auxiliary language classification loss to further enhance the model's ability to distinguish between different languages.

• Key Findings:

  • Evaluated on the 7-language Multilingual Librispeech (MLS) dataset, csvMASR achieves a new state-of-the-art word error rate (WER) of 9.95%, outperforming existing MASR models.
  • csvMASR demonstrates superior performance in language classification tasks, achieving up to 16.65% higher accuracy compared to the Framewise weighted interpolation model.
  • Language prompting experiments highlight the model's configurability, with a minimal WER gap (< 1%) observed between 1-hot and all-hot LID decoding scenarios.

• Main Conclusions: The authors conclude that csvMASR effectively addresses key challenges in multilingual ASR, demonstrating improved performance, configurability, and language classification accuracy. The use of speech summary vector representations and adapter modules proves beneficial for capturing language-specific features and enhancing overall model performance.

• Significance: This research significantly contributes to the field of multilingual ASR by proposing a novel and effective model architecture that outperforms existing approaches. The findings have practical implications for developing more accurate and adaptable speech recognition systems for multilingual scenarios.

• Limitations and Future Research: While csvMASR shows promising results, the authors suggest exploring its scalability to a larger number of languages and investigating the impact of different adapter placement strategies within the model architecture as potential avenues for future research.

Statistikk

Approximately half of the world’s population is multilingual.
csvMASR reduces the baseline WER from 10.33% to 9.95% on a 7-language setup.
csvMASR also performs strongly in language classification with up to 16.65% higher accuracy than the Framewise model.
Language prompting tasks demonstrate its configurability, with a WER gap of < 1% between 1-hot and all-hot LID inference.

Sitater

"This has motivated the study of multilingual ASR (MASR) models as an alternative option. However, naively training a MASR model with pooled multilingual training data usually leads to inferior performance compared to monolingual models [2]–[4], which can be due to language confusion and bias towards data-rich languages [3]."
"In this work, we propose a novel configurable MASR model: Configurable MASR model with Summary Vector (csvMASR)."

Viktige innsikter hentet fra

Configurable Multilingual ASR with Speech Summary Representations

by Harrison Zhu... klokken arxiv.org 10-08-2024

https://arxiv.org/pdf/2410.04478.pdf

Dypere Spørsmål

How might the csvMASR model be adapted for use in low-resource language settings where training data is limited?

Adapting csvMASR for low-resource languages presents challenges due to its reliance on data-driven components like adapters and the summary vector. Here are potential strategies:

Cross-lingual Transfer Learning:

Shared Encoder: Train the Conformer encoder on high-resource languages and adapt only the language-specific adapters and summary vector classifier on the low-resource language. This leverages phonetic and linguistic similarities across languages.
Adapter Initialization: Instead of random initialization, initialize adapters for low-resource languages using adapters from phonetically similar high-resource languages. This provides a starting point closer to the target language's acoustic space.

Data Augmentation:

Synthetic Data: Generate synthetic speech data for the low-resource language using techniques like text-to-speech (TTS) or voice conversion. This augments the limited training data, improving model robustness.
Back-translation: Translate existing high-resource language transcripts into the low-resource language, creating parallel data for training. This leverages available resources to indirectly benefit low-resource ASR.

Multilingual Training with Resource Balancing:

Weighted Sampling: During training, oversample the low-resource language data to counter the data imbalance. This ensures the model pays sufficient attention to the under-represented language.
Curriculum Learning: Start training with high-resource languages and gradually introduce the low-resource language, allowing the model to first learn general acoustic patterns before specializing.

Parameter Sharing and Reduction:

Adapter Pruning: After initial training, prune less important adapter parameters to reduce model size and prevent overfitting on limited data.
Low-Rank Factorization: Decompose adapter matrices into lower-rank representations, reducing parameter count while preserving essential information.

By combining these approaches, csvMASR can be tailored for low-resource scenarios, improving its performance even with limited training data.

Could the reliance on language ID vectors in csvMASR be considered a limitation in scenarios where accurate language identification is challenging?

Yes, csvMASR's reliance on language ID (LID) vectors can be a limitation when accurate LID is challenging. Here's why:

Performance Degradation with Incorrect LIDs: csvMASR heavily depends on accurate LIDs to activate the correct language-specific adapters. If the provided LID is incorrect, the model will activate the wrong adapters, leading to significant performance degradation in both ASR and language classification.
Sensitivity to LID Accuracy: The model's performance is directly tied to the accuracy of the LID system. In real-world scenarios with noisy environments or code-switching, achieving high LID accuracy can be difficult, impacting csvMASR's effectiveness.
Limited Applicability in LID-Agnostic Scenarios: In situations where LID information is unavailable or unreliable, csvMASR's architecture offers no inherent mechanism to handle language ambiguity. This limits its applicability in LID-agnostic scenarios.
Mitigation Strategies:

Robust LID Systems: Employing highly accurate and robust LID systems is crucial to minimize errors propagated to csvMASR. This might involve using ensemble methods or context-aware LID models.
Uncertainty Handling: Incorporating uncertainty handling mechanisms within csvMASR could mitigate the impact of incorrect LIDs. For example, instead of binary LID vectors, using probability distributions over languages could allow the model to consider multiple language hypotheses.
Joint LID and ASR Optimization: Training the LID and ASR components jointly could lead to better overall performance and potentially allow the model to learn more robust representations less sensitive to LID errors.
Addressing these limitations is essential for deploying csvMASR in real-world applications where accurate LID is not always guaranteed.

How might the concept of speech summary vector representations be applied to other speech-related tasks beyond automatic speech recognition, such as speaker verification or emotion recognition?

The concept of speech summary vector representations, successfully used in csvMASR for capturing utterance-level language information, holds promise for other speech-related tasks:
1. Speaker Verification:

Utterance-level Speaker Embeddings: Instead of averaging frame-level features, a summary vector could learn a fixed-length representation of the speaker's vocal characteristics across the entire utterance. This could lead to more robust speaker embeddings, less sensitive to within-utterance variations.
Speaker Change Detection: By training a model to distinguish between summary vectors from different speakers, it could be used for speaker change detection in multi-speaker scenarios.
2. Emotion Recognition:

Global Emotional Context: Similar to language, emotions often manifest across entire utterances. A summary vector could capture the overall emotional tone, complementing frame-level emotional cues.
Speaker-dependent Emotion Recognition: By incorporating speaker information into the summary vector, the model could learn speaker-specific emotional patterns, improving personalized emotion recognition.
3. Other Applications:

Speech Segmentation: Summary vectors could be used to segment speech into homogeneous units based on acoustic characteristics, useful for tasks like topic segmentation or diarization.
Speech Quality Assessment: By learning to predict speech quality metrics from summary vectors, the model could provide an overall quality score for an utterance, aiding in speech enhancement or quality monitoring.
Implementation Considerations:

Task-Specific Encoding: The architecture of the summary vector module might need adjustments depending on the task. For example, using recurrent layers or attention mechanisms could be beneficial for capturing temporal dynamics in emotion recognition.
Multi-task Learning: Training the summary vector jointly with the primary task (e.g., speaker verification or emotion recognition) could lead to more informative representations.
By adapting the concept of speech summary vectors to different speech processing tasks, we can potentially improve performance by capturing global, utterance-level information that complements traditional frame-level analysis.