Improving Lattice Rescoring in Low Resource ASR

Kernkonzepte

Initial decoding with a minimally augmented language model followed by lattice rescoring significantly reduces word error rate in low-resource languages.

Zusammenfassung

Automatic speech recognition systems for low-resource languages face challenges due to small corpora. Decoding with a larger language model is memory-intensive. Augmenting the baseline LM with unigram counts of OOT words improves lattice comprehensiveness. The proposed method shows significant word error reduction for Telugu and Kannada. Rescoring lattices with a larger LM results in comparable WER reduction to full Wikipedia text augmentation but consumes less memory. The approach is consistent across different datasets and computationally less expensive.

Statistiken

Larger language models give only marginal improvement.
Proposed method achieves 21.8% (Telugu) and 41.8% (Kannada) relative word error reduction.
Reduction in word error rate comparable to full Wikipedia text augmentation while consuming only 1/8th the memory.

Zitate

"We obtain a significant reduction in error for low-resource Indic languages, namely, Kannada and Telugu."
"Our approach is applicable for training speech recognition systems under low resource conditions."

Wichtige Erkenntnisse aus

Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR

by Savitha Murt... um arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10937.pdf

Tiefere Fragen

How can this method be adapted for other low-resource languages?

The method of minimal LM augmentation can be adapted for other low-resource languages by following a similar approach of enhancing the baseline language model with unigram counts of out-of-train words from a larger text corpus. This involves identifying OOV words in the target language, selecting relevant text data from sources like Wikipedia or web resources, and augmenting the baseline LM to include these additional vocabulary items. By performing initial decoding with this minimally augmented LM and then rescoring the lattices with a larger language model, significant improvements in word error rate (WER) reduction can be achieved. This approach is particularly beneficial for languages with limited linguistic resources and high OOV rates.

What are the implications of reducing computational resources in speech recognition systems?

Reducing computational resources in speech recognition systems has several important implications:

Cost-Effectiveness: By minimizing the computational requirements, organizations can save on hardware costs and operational expenses associated with running resource-intensive ASR models.
Scalability: Systems that require fewer computational resources are more scalable, allowing for easier expansion and adaptation to changing needs without significant investments.
Efficiency: Reduced computational demands lead to faster processing times, enabling real-time or near-real-time speech recognition applications.
Accessibility: Lower resource requirements make advanced ASR technology more accessible to users who may have limited access to high-performance computing infrastructure.
Environmental Impact: Decreased energy consumption due to reduced computational load contributes positively towards environmental sustainability efforts.

How can the concept of minimal LM augmentation be applied to improve other aspects of ASR beyond WER reduction?

The concept of minimal LM augmentation can be extended to enhance various aspects of Automatic Speech Recognition (ASR) beyond Word Error Rate (WER) reduction:

OOV Handling: By focusing on out-of-vocabulary word recovery through targeted language model enhancements, overall vocabulary coverage improves, leading to better transcription accuracy.
Named Entity Recognition: Incorporating named entities into the augmented language model helps recognize specific terms or entities that may not exist in standard vocabularies but are crucial for certain domains or applications.
Contextual Understanding: Augmenting LMs with domain-specific data enables better contextual understanding during transcription tasks related to specialized fields such as medicine, law, finance, etc.
Speaker Adaptation: Adapting language models based on speaker characteristics or dialects enhances personalization in ASR systems by improving accuracy when transcribing diverse voices.
5Language Model Adaptation: The concept could also extend towards dynamic adaptation where LMs evolve over time based on user interactions and feedback loops resulting in continuous improvement in system performance.

By applying minimal LM augmentation creatively across different facets of ASR development and deployment processes, it's possible to achieve comprehensive enhancements that go beyond mere WER reduction alone.