Effective Internal Language Model Training and Fusion for Factorized Transducer Models Improves Automatic Speech Recognition Performance

Core Concepts

The authors propose a novel internal language model (ILM) training and decoding strategy for factorized transducer models, which effectively combines the blank, acoustic, and ILM scores to achieve substantial performance improvements in automatic speech recognition.

Abstract

The paper presents a novel approach for training and decoding factorized transducer (FT) models for automatic speech recognition (ASR). The key highlights are:

1. ILM Training:

   • The authors first pre-train the non-blank predictor of the FT model using ILM loss on large-scale text-only data.
   • This pre-trained ILM is then fixed during the joint optimization of the FT model with RNN-T loss.

2. Proposed ILM Fusion Strategy:

   • The authors propose a new decoding strategy that effectively combines the blank, acoustic, and ILM scores.
   • This involves applying two ILM weights: one to scale the ILM score within the non-blank score calculation, and another to add the ILM score externally.
   • The optimal weights are selected through experiments.

3. ILM-fusion-aware MWER Training:

   • To further improve the ILM integration, the authors propose a novel and memory-efficient ILM-fusion-aware minimum word error rate (MWER) training method.
   • This leverages the estimated maximum-probability alignment from beam search decoding to restrict the alignment summation paths, reducing memory usage.

The experiments on the LibriSpeech dataset show that the proposed approach leads to a 17% relative improvement over the standard decoding method for factorized transducer models. Furthermore, it outperforms a strong RNN-T baseline with external language model fusion, achieving a 5.5% relative improvement on general test sets and an 8.9% WER reduction for rare words.

Stats

The proposed ILM fusion strategy with optimized weights (β=0.6, α=0.6) leads to a 17% relative improvement over the standard decoding method for factorized transducer models on LibriSpeech datasets.
Compared to a strong RNN-T baseline with external language model fusion, the proposed model yields a 5.5% relative improvement on general test sets and an 8.9% WER reduction for rare words.

Quotes

"The proposed model can achieve superior performance without relying on external language models, rendering it highly efficient for production use-cases."
"To further improve the performance, we propose a novel and memory-efficient ILM-fusion-aware minimum word error rate (MWER) training method which improves ILM integration significantly."

Key Insights Distilled From

Effective internal language model training and fusion for factorized transducer model

by Jinxi Guo,Ni... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01716.pdf

Deeper Inquiries

How can the proposed ILM training and fusion strategy be extended to other end-to-end speech recognition models beyond factorized transducer models?

The proposed ILM training and fusion strategy can be extended to other end-to-end speech recognition models by adapting the concept of training an internal language model (ILM) separately and then integrating it effectively during decoding. For models like connectionist temporal classification (CTC) based models or attention-based seq2seq models, a similar approach can be applied. The key is to train the ILM with text-only data to enhance the model's language understanding capabilities. During decoding, incorporating the ILM scores alongside acoustic and other model scores can help improve the overall performance. By pre-training the ILM and optimizing its integration with the main model, the strategy can be applied to various end-to-end architectures to boost recognition accuracy and efficiency.

What are the potential challenges and limitations of the alignment-restricted MWER training approach, and how can it be further improved?

The alignment-restricted Minimum Word Error Rate (MWER) training approach faces challenges related to memory efficiency and alignment accuracy. Limitations may arise from the need for predefined left and right contexts, which can impact the model's ability to capture long-range dependencies accurately. Additionally, estimating alignments from beam search decoding introduces potential errors that could affect the training process. To address these challenges and improve the approach, several strategies can be implemented. One approach is to refine the alignment estimation method to enhance accuracy, possibly by incorporating feedback mechanisms or refining the alignment calculation process. Moreover, optimizing the balance between memory efficiency and alignment coverage is crucial. Techniques like dynamic context adjustment based on alignment patterns or adaptive context selection can help improve the effectiveness of the alignment-restricted MWER training approach.

Given the significant performance gains on rare words, how can the proposed techniques be leveraged to improve the recognition of domain-specific or out-of-vocabulary terms in practical speech recognition applications?

The proposed techniques that show significant performance gains on rare words can be leveraged to enhance the recognition of domain-specific or out-of-vocabulary terms in practical speech recognition applications by focusing on specialized language modeling and training strategies. One approach is to tailor the ILM training on domain-specific data or vocabulary to improve the model's understanding of specialized terms. Additionally, incorporating domain-specific external language models during fusion can further enhance recognition accuracy for out-of-vocabulary terms. Fine-tuning the ILM fusion strategy to give more weight to domain-specific terms or adjusting the decoding process to prioritize rare or specialized words can also be beneficial. By customizing the training data, language models, and fusion techniques to the specific domain or vocabulary, the proposed techniques can effectively address the challenges of recognizing domain-specific or out-of-vocabulary terms in practical speech recognition scenarios.