Optimizing Contextual Speech Recognition: Efficient Retrieval Using Vector Quantization for Large Biasing Catalogues

Core Concepts

This paper introduces a novel approach to enhance contextual speech recognition by leveraging vector quantization for efficient retrieval from large biasing catalogues, thereby improving recognition accuracy, especially for named entities, while significantly reducing computational costs.

Abstract

Research Paper Summary

Bibliographic Information: Flemotomos, N., Hsiao, R., Swietojanski, P., Hori, T., Can, D., & Zhuang, X. (2024). Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval. arXiv preprint arXiv:2411.00664.

Research Objective: This paper aims to address the computational challenges of neural contextual biasing (NCB) in speech recognition, particularly when dealing with large catalogues of biasing entries like contact names or media titles.

Methodology: The authors propose a two-stage approach:

1. Quantization and Retrieval: They utilize Finite Scalar Quantization (FSQ) to discretize contextual embeddings, enabling efficient retrieval of relevant biasing entries from a large catalogue based on their dot-product similarity with acoustic encodings.
2. Contextual Biasing: The retrieved entries are then used for biasing the speech recognition model, either through traditional cross-attention or by prompting a Large Language Model (LLM) in a delayed fusion setup.

Key Findings:

• The proposed FSQ-based retrieval method achieves comparable accuracy to traditional dense cross-attention biasing while significantly reducing computation time (20% faster) and memory usage (85-95% reduction) for large biasing lists.
• Retrieval NCB effectively scales to large biasing catalogues, showing up to 71% relative error rate reduction in personal entity recognition.
• The approach is flexible and compatible with various biasing mechanisms (cross-attention, LLM prompting) and decoding configurations (auto-regressive, non-auto-regressive).

Main Conclusions:

• Vector quantization, specifically FSQ, is a viable technique for efficient and accurate retrieval in contextual speech recognition.
• The proposed Retrieval NCB method allows for leveraging large biasing catalogues, leading to improved recognition accuracy, especially for named entities.
• The flexibility of the approach makes it easily adaptable to different ASR architectures and decoding strategies.

Significance: This research significantly contributes to the field of contextual speech recognition by enabling the use of extensive biasing catalogues, which was previously computationally infeasible. This opens up new possibilities for improving the accuracy and personalization of speech-based applications.

Limitations and Future Research: While the paper demonstrates the effectiveness of FSQ for retrieval, future research could explore other quantization techniques and their impact on accuracy and efficiency. Additionally, investigating the performance of the proposed method on other datasets and languages would be beneficial.

Stats

The proposed approach achieves over 20% speed boost and 85-95% memory usage reduction for lists of up to one million entries compared to standard dot-product cross-attention.
Retrieval based shortlisting allows for up to 71% relative error rate reduction in personal entity recognition.
The largest biasing catalogues in the test set contain around 22.6k entries.
Enumerating biasing phrases into word- and word-order-level combinations reduces NEER on contacts by 9.7% relative.

Quotes

"Neural contextual biasing (NCB) offers an alternative paradigm where the biasing mechanism is part of the ASR model and jointly learned with the main ASR objective."
"This work addresses the attention-driven computational limitations of NCB employing a quantization-based, two-stage approach."
"Our work opens up a path to scaling neural contextualization to relatively unexplored scenarios such as biasing towards large media catalogues, where the number of entries could be in the hundreds of thousands or millions."

Key Insights Distilled From

Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval

by Nikolaos Fle... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00664.pdf

Deeper Inquiries

How would the proposed approach perform in real-world scenarios with noisy audio and spontaneous speech, where the accuracy of the initial retrieval might be affected?

This is a crucial point that the paper acknowledges indirectly but doesn't explicitly test. Here's a breakdown of the potential issues and mitigations:

Noise Impact: Noisy audio degrades the performance of any ASR system, including the acoustic encoder used for retrieval.

Consequence: The acoustic embeddings (queries) will be less accurate, leading to mismatches with the quantized contextual embeddings.
Mitigation:

Robust Training: Training on data augmented with noise can improve the encoder's resilience.
Signal Enhancement: Pre-processing audio with noise reduction techniques could help.
Larger K: Increasing the number of retrieved candidates (K) might compensate for some retrieval errors, at the cost of more computation.

Spontaneous Speech: Disfluencies, hesitations, and out-of-vocabulary words are common in spontaneous speech.

Consequence: The initial ASR output used to guide retrieval might be error-prone, again leading to poor retrieval.
Mitigation:

Language Model Adaptation: Fine-tuning the language model on spontaneous speech data can make it more tolerant to disfluencies.
Phonetic Retrieval: Exploring retrieval based on phonetic similarity (as mentioned in the paper) could be more robust than relying solely on word-level matching.

Evaluation on Realistic Data: The paper's evaluation is on a voice assistant dataset, which might not fully represent the challenges of noisy or highly spontaneous speech. Testing on more diverse and challenging datasets would provide a more realistic assessment.
In summary, while the proposed approach shows promise, its robustness to real-world conditions needs further investigation. The mitigations mentioned above could be explored to enhance its reliability.

Could the reliance on pre-trained models and fixed biasing catalogues limit the adaptability of this approach to dynamic situations or user-specific vocabulary changes over time?

Yes, this is a valid concern. Here's a closer look:

Pre-trained Model Limitations:

Domain Shift: If the pre-trained acoustic and contextual encoders are trained on data significantly different from the target domain, their performance might degrade.
Vocabulary Mismatch: New words or phrases not seen during training might not be recognized or encoded effectively.

Fixed Biasing Catalogue Issues:

Static Nature: User-specific vocabulary and preferences evolve over time. A fixed catalogue becomes outdated, reducing the effectiveness of contextual biasing.
Cold Start Problem: New users lack a biasing catalogue, making the system less useful initially.
Addressing Adaptability:

On-the-fly Updates:

Dynamic Catalogues: Allow users to easily add, remove, or modify entries in their biasing catalogues.
Incremental Learning: Explore techniques to update the quantized representations and potentially fine-tune the model with new user data, without requiring full retraining.

Personalization:

User-Specific Models: In the future, with more efficient training, having user-specific models could become feasible, addressing domain and vocabulary shifts more effectively.
Federated Learning: Explore privacy-preserving techniques like federated learning to adapt models to user data without directly accessing their private information.
In conclusion, while the current approach relies on pre-trained models and fixed catalogues, incorporating mechanisms for dynamic updates and personalization is essential for long-term adaptability and user satisfaction.

What are the ethical implications of using large biasing catalogues, especially concerning user privacy and potential biases embedded in the data used to train these models?

The use of large biasing catalogues in ASR, while beneficial for accuracy, raises significant ethical concerns:

Privacy:

Sensitive Information: Biasing catalogues often contain highly personal data (contacts, app usage, media preferences). Leakage or unauthorized access to this data would be a severe privacy violation.
Inference Attacks: Even if catalogues are stored securely, an attacker could potentially infer sensitive information based on the ASR system's outputs. For example, consistently recognizing a specific name might reveal a close relationship.

Bias and Fairness:

Training Data Bias: If the training data for the acoustic and contextual encoders contains biases (e.g., under-representation of certain accents or demographics), the resulting ASR system might perpetuate these biases, leading to unfair or discriminatory outcomes.
Catalogue Bias: User-generated biasing catalogues could reflect their own biases, potentially leading to the system reinforcing those biases in its outputs.
Mitigating Ethical Risks:

Privacy-Preserving Techniques:

On-device Processing: Process biasing catalogues locally on the user's device to minimize data transfer and potential exposure.
Differential Privacy: Add noise to the training process or the quantized representations to make it harder to infer sensitive information from the model or its outputs.
Federated Learning: Train models across multiple devices without directly accessing user data, enhancing privacy.

Bias Mitigation:

Diverse Training Data: Use training data that is representative of diverse accents, dialects, and demographic groups to reduce bias in the acoustic and contextual models.
Bias Detection and Correction: Develop methods to detect and mitigate biases in both the training data and the user-generated biasing catalogues.
Transparency and Explainability: Make the biasing mechanisms more transparent to users, allowing them to understand and potentially correct for potential biases.
Ethical Considerations are Paramount: It's crucial to acknowledge and address these ethical implications proactively. Striking a balance between improved ASR accuracy and user privacy, fairness, and responsible AI development is essential.