Efficient Speech Recognition with Skipformer Strategy

Core Concepts

The author proposes the Skip-and-Recover strategy using the Skipformer architecture to dynamically reduce sequence input length and improve recognition accuracy in speech recognition tasks.

Abstract

The Skipformer strategy aims to address the computational overload caused by long input sequences in Conformer-based attention models for Automatic Speech Recognition. By splitting frames into crucial, skipping, and ignoring groups based on an intermediate CTC output, the model achieves a significant reduction in input sequence length while maintaining recognition accuracy. Experimental results demonstrate improved efficiency and faster inference speed compared to baseline models like Conformer and Efficient Conformer. The proposed approach shows promise in enhancing computational efficiency without compromising performance.

Stats

Our model reduces the input sequence length by 31 times on Aishell-1 and 22 times on Librispeech corpus. The proposed Skipformer achieved 4.23% CER on Aishell-1 test set with an 8% relative CER reduction. Mode 2 of Skipformer was verified to be computationally efficient with the best recognition performance.

Quotes

"Our proposed Skipformer consists of two main operations: frame skipping and recovering." "The less useful information one frame contains, the simpler model required to model it." "The more crucial information one frame contains, the more complex model required to model it."

Key Insights Distilled From

Skipformer

by Wenjing Zhu,... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08258.pdf

Deeper Inquiries

How does the introduction of a blank symbol impact the alignment of input and output sequences in ASR models

In ASR models, the introduction of a blank symbol plays a crucial role in aligning input and output sequences. The blank symbol is typically used to handle cases where the length of the input sequence exceeds that of the output sequence. By inserting a blank symbol, it allows for proper alignment between the two sequences during training and decoding processes. This alignment ensures that the model can learn to predict accurately by accounting for variable lengths in speech utterances.

What are potential drawbacks or limitations of using importance sampling methods like CTC guidance for downsampling frames

While importance sampling methods like CTC guidance can be beneficial for downsampling frames in ASR models, they also come with potential drawbacks and limitations. One limitation is that using CTC guidance may lead to performance drops after decoding without blank symbols. Additionally, there might be challenges in determining an optimal threshold for classifying frames as important or not based on their intermediate CTC output probabilities. Moreover, relying solely on importance sampling methods could result in oversimplification or loss of critical information needed for accurate recognition.

How can the concept of "Skip-and-Recover" be applied to other domains beyond speech recognition for enhanced efficiency

The concept of "Skip-and-Recover" introduced in speech recognition models can be applied to other domains beyond ASR for enhanced efficiency. For instance: In computer vision tasks: Skip-and-Recover could be utilized to dynamically skip over less relevant image regions while focusing computational resources on crucial areas during processing. Natural language processing: Implementing Skip-and-Recover strategies could help streamline text analysis by prioritizing key semantic elements while disregarding redundant or irrelevant information. Time-series data analysis: Applying Skip-and-Recover techniques could optimize processing large temporal datasets by selectively skipping less informative time steps and recovering essential patterns efficiently. By adapting this approach across various domains, organizations can potentially improve computational efficiency without compromising accuracy or performance metrics.