Building a World-English Language Model for On-Device Virtual Assistants

Core Concepts

Combining regional variants of English to create a "World English" NNLM for on-device VAs improves scalability and accuracy.

Abstract

Neural Network Language Models (NNLMs) for Virtual Assistants (VAs) are typically language-, region-, and device-dependent.
Combining regional variants of English can enhance scalability by reducing the number of models needed.
Adapter modules are effective in modeling dialects in NNLMs.
FOFE-based models are preferred for Speech-to-Text (STT) and Assistant applications.
The study focuses on building a multi-dialect model for high-resourced English dialects.
The proposed architecture, AD+CAA+DA, offers a favorable accuracy-latency-memory trade-off.
The model improves accuracy on all test sets and matches the latency and memory constraints of on-device VAs.

Stats

Combining regional variants of English to build a “World English” NNLM for on-device VAs.
Adapter modules are more effective in modeling dialects than specializing entire sub-networks.
FOFE-based models have a better accuracy-latency trade-off for Speech-to-Text (STT) and Assistant applications.

Quotes

"Adapter modules are more effective in modeling dialects than specializing entire sub-networks."
"The proposed architecture, AD+CAA+DA, offers a favorable accuracy-latency-memory trade-off."

Key Insights Distilled From

Towards a World-English Language Model for On-Device Virtual Assistants

by Rricha Jalot... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18783.pdf

Deeper Inquiries

How can the World-English NNLM be further expanded to include more dialects and languages?

To expand the World-English NNLM to include more dialects and languages, a systematic approach can be followed. Firstly, data curation and sampling techniques similar to those used in the study can be applied to gather a diverse dataset encompassing various dialects and languages. The training data should be balanced across different dialects and languages to ensure fair representation.
Next, the existing architecture can be modified to accommodate the additional dialects and languages. Adapter modules can be extended or redesigned to cater to the specific characteristics of new dialects and languages. Bayesian Optimization or similar techniques can be employed to determine the optimal placement and compression dimensions for the adapters.
Furthermore, the training strategies for adapters can be refined and adapted to the new dataset. Pre-training adapters on a combined dataset before fine-tuning on dialect-specific data can be a viable strategy. The architecture can be adjusted to incorporate dual adapters or common application adapters to capture shared traits across dialects and languages.

What are the potential drawbacks of relying on adapter modules for modeling dialects in NNLMs?

While adapter modules offer a parameter-efficient way to model dialect-specific characteristics in NNLMs, there are potential drawbacks to consider. One drawback is the complexity of determining the optimal placement of adapters within the architecture. The effectiveness of adapters can vary based on their position in the network, and finding the right configuration may require extensive experimentation.
Another drawback is the need for careful training strategies for adapters. The process of pre-training and fine-tuning adapters on dialect-specific data can be resource-intensive and time-consuming. Additionally, the performance of adapters may depend on the quality and quantity of training data available for each dialect, which can pose challenges in low-resource language scenarios.
Moreover, the interpretability of adapter modules can be a concern. Understanding how adapters capture and represent dialect-specific features within the neural network may require additional analysis and visualization techniques, adding complexity to the model evaluation process.

How can the insights from this study be applied to improve multilingual ASR systems beyond English dialects?

The insights from this study can be valuable for enhancing multilingual ASR systems beyond English dialects. One key application is the adaptation of adapter modules to model dialect-specific traits in other languages. By following a similar approach of combining regional variants and leveraging adapter modules, multilingual ASR systems can be tailored to capture diverse linguistic variations.
Additionally, the proposed architecture with shared representations for applications and dialects can be extended to multilingual settings. By incorporating common application adapters and dual adapters, the model can learn shared features across languages while maintaining language-specific characteristics.
Furthermore, the training strategies and optimization techniques used in the study can be applied to train multilingual ASR models effectively. By curating diverse datasets, balancing data representation, and fine-tuning adapters on specific language data, the performance of multilingual ASR systems can be improved across a wide range of languages and dialects.