Enhancing Hybrid Autoregressive Transducer-based Automatic Speech Recognition with Internal Acoustic Model Training and Dual Blank Thresholding

Core Concepts

Jointly training Hybrid Autoregressive Transducer (HAT) with various Connectionist Temporal Classification (CTC) objectives, including the proposed Internal Acoustic Model (IAM), improves HAT-based automatic speech recognition performance. Deploying dual blank thresholding, which combines HAT-blank and IAM-blank thresholding, along with a compatible decoding algorithm, achieves a 42-75% increase in decoding speed without significant degradation in accuracy.

Abstract

The paper proposes two approaches to enhance Hybrid Autoregressive Transducer (HAT)-based automatic speech recognition (ASR):

1. Joint training with various CTC objectives:

   • The authors investigate joint training of HAT with vanilla CTC, Factorized CTC (FCTC), and the proposed Internal Acoustic Model (IAM).
   • Experiments on the TED-LIUM and LibriSpeech datasets show that joint training with CTC objectives, especially with larger weights for IAM, leads to statistically significant improvements in HAT performance compared to standalone HAT.
   • The authors hypothesize that the alignment information provided by CTC is more beneficial for HAT, which models blank and non-blank distributions separately, than for the standard Recurrent Neural Network Transducer (RNNT) with a single distribution.

2. Dual blank thresholding with compatible decoding:

   • The authors introduce dual blank thresholding, which combines HAT-blank thresholding and IAM-blank thresholding, to further enhance decoding speed.
   • HAT-blank thresholding skips the computation of non-blank probabilities based on the HAT blank posterior, while IAM-blank thresholding does the same based on the IAM blank posterior.
   • The authors also investigate alignment-length synchronous decoding (ALSD) and time-synchronous decoding (TSD) algorithms to mitigate the performance degradation caused by erroneous frame-skipping in blank thresholding.
   • Experiments show that the proposed dual blank thresholding with TSD achieves a 42-75% increase in decoding speed on the TED-LIUM and LibriSpeech datasets without significant degradation in ASR performance.

Overall, the paper demonstrates that joint training of HAT with CTC objectives, especially IAM, and the proposed dual blank thresholding with compatible decoding can effectively enhance the performance and efficiency of HAT-based ASR systems.

Stats

The authors report the following key statistics:

The oracle non-blank percentage (NBP) was 16% for TED-LIUM and 14% for LibriSpeech.
Deploying dual blank thresholding with the best configurations achieved a 72% decoding speed-up for the offline TED-LIUM system and a 75% speed-up for the offline LibriSpeech system, compared to HAT without any blank thresholding.
For the streaming systems, the proposed approaches achieved a 42% decoding speed-up for TED-LIUM and a 45% speed-up for LibriSpeech, compared to HAT without blank thresholding.

Quotes

"Applying CTC objectives to each neural transducer training (α > 0) resulted in improvements in both RNNT and HAT. RNNT with α = 0.25 and HAT with α = 0.75 achieved the best WERs."
"IAM in HAT exhibits less degradation than FCTC for CTC-blank thresholding in both offline and streaming modes. This is probably because IAM was jointly trained with HAT and shared all network parameters. Therefore, the blank emission timings of IAM can be synchronized with those of HAT more effectively than FCTC."

Key Insights Distilled From

Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding

by Takafumi Mor... at arxiv.org 10-01-2024

https://arxiv.org/pdf/2409.20313.pdf

Deeper Inquiries

How could the proposed joint training and dual blank thresholding approaches be extended to other types of end-to-end speech recognition models, such as attention-based encoder-decoder architectures?

The proposed joint training and dual blank thresholding approaches can be effectively extended to attention-based encoder-decoder architectures by leveraging the inherent characteristics of these models. In attention-based systems, the encoder processes the input sequence and generates context vectors that the decoder uses to produce the output sequence. Here are several strategies for adaptation:

Joint Training with CTC Objectives: Similar to the hybrid autoregressive transducer (HAT) model, attention-based architectures can benefit from joint training with Connectionist Temporal Classification (CTC) objectives. By incorporating CTC loss alongside the attention mechanism, the model can learn to align input and output sequences more effectively, improving overall performance. This can be achieved by introducing a shared encoder that outputs both attention-based and CTC predictions, allowing for synchronized learning.

Dual Blank Thresholding: The dual blank thresholding approach can be adapted by implementing a mechanism that allows the attention model to skip non-blank computations based on a threshold. For instance, during decoding, if the attention model predicts a low probability for a blank token, the model can bypass the computation of subsequent tokens, similar to how it operates in HAT. This would require modifications to the attention mechanism to support parallel processing of frames, thus enhancing decoding speed.

Parameter Sharing: By sharing parameters between the encoder and the decoder, as done in the Internal Acoustic Model (IAM) framework, attention-based models can reduce the number of parameters and improve efficiency. This can be particularly beneficial in scenarios where computational resources are limited, allowing for faster inference without sacrificing accuracy.

Incorporating Frame-Skipping Techniques: Attention-based models can also implement frame-skipping techniques similar to those used in HAT. By analyzing the output of the attention mechanism, the model can determine when to skip frames based on the predicted probabilities of blank and non-blank tokens, thereby optimizing the decoding process.

By integrating these strategies, attention-based encoder-decoder architectures can achieve improved performance and efficiency, similar to the advancements seen in HAT with IAM and dual blank thresholding.

What are the potential limitations or drawbacks of the IAM framework, and how could it be further improved to enhance its performance and efficiency?

While the Internal Acoustic Model (IAM) framework presents several advantages, it also has potential limitations that could impact its performance and efficiency:

Increased Complexity: The IAM framework introduces additional complexity due to the need for joint training with multiple objectives (e.g., CTC and HAT). This complexity can lead to longer training times and may require careful tuning of hyperparameters to achieve optimal performance.

Dependency on Synchronous Emission: The effectiveness of IAM relies on the synchronous emission of blank symbols with the HAT decoder. If the alignment between the two models is not well maintained, it could lead to performance degradation, particularly in scenarios with noisy or variable input data.

Limited Flexibility: The IAM framework is designed specifically for HAT, which may limit its applicability to other architectures. Adapting IAM for different end-to-end models may require significant modifications, which could hinder its widespread adoption.

Potential for Overfitting: With the increased number of parameters due to shared networks, there is a risk of overfitting, especially when training on smaller datasets. This could lead to reduced generalization capabilities in real-world applications.

To enhance the performance and efficiency of the IAM framework, the following improvements could be considered:

Regularization Techniques: Implementing regularization methods such as dropout or weight decay can help mitigate overfitting and improve generalization.

Adaptive Learning Rates: Utilizing adaptive learning rate strategies can optimize the training process, allowing the model to converge more quickly and effectively.

Data Augmentation: Incorporating data augmentation techniques can enhance the robustness of the model by exposing it to a wider variety of training examples, thereby improving its performance on unseen data.

Modular Design: Developing a more modular IAM framework that can be easily adapted to different architectures would increase its flexibility and usability across various end-to-end speech recognition models.
By addressing these limitations and implementing improvements, the IAM framework can be made more robust and efficient, leading to better performance in practical applications.

Given the significant improvements in decoding speed, how could the proposed techniques be leveraged to enable real-time, low-latency speech recognition in practical applications?

The proposed techniques, particularly the joint training and dual blank thresholding approaches, can significantly enhance real-time, low-latency speech recognition in practical applications through the following strategies:

Optimized Decoding Algorithms: By employing the compatible decoding algorithms such as Alignment-Length Synchronous Decoding (ALSD) and Time-Synchronous Decoding (TSD), the system can efficiently manage the trade-off between speed and accuracy. These algorithms can be fine-tuned to prioritize low-latency responses, making them suitable for applications like virtual assistants and real-time transcription services.

Dynamic Thresholding: Implementing dynamic blank thresholding allows the system to adaptively adjust the thresholds based on the input characteristics and context. This flexibility can help maintain high accuracy while minimizing latency, particularly in environments with varying noise levels or speech patterns.

Parallel Processing: The dual blank thresholding approach enables parallel processing of frames, which can be leveraged to reduce the computational load during inference. By skipping unnecessary computations for non-blank tokens, the system can achieve faster response times, making it ideal for applications requiring immediate feedback, such as interactive voice response systems.

Edge Computing: Deploying the enhanced models on edge devices can facilitate real-time processing without the need for constant cloud connectivity. This is particularly beneficial for mobile applications and IoT devices, where low latency is critical for user experience.

Resource Allocation: By optimizing resource allocation based on the decoding speed improvements, systems can prioritize processing power for speech recognition tasks during peak usage times, ensuring that latency remains low even under heavy loads.

User-Centric Design: Tailoring the speech recognition system to user needs, such as adjusting the balance between speed and accuracy based on user preferences, can enhance the overall experience. For instance, in scenarios where immediate feedback is crucial, the system can prioritize speed, while in more complex tasks, it can focus on accuracy.

By leveraging these techniques, real-time, low-latency speech recognition can be effectively implemented in various practical applications, enhancing user interaction and satisfaction.