[Paper Review] An Improved Residual LSTM Architecture for Acoustic Modeling

Lu Huang, Jiasong Sun+2 more|arXiv (Cornell University)|Aug 17, 2017

Speech Recognition and Synthesis3 citations

TL;DR

This paper proposes an improved residual Long Short-Term Memory (LSTM) architecture for acoustic modeling in automatic speech recognition, integrating residual connections into LSTM units to enhance training stability and performance. Experiments show a relative 8% reduction in Phone Error Rate (PER) on TIMIT and 4% on the residual fast LSTM variant, with strong generalization across multiple datasets including THCHS-30, Librispeech, and Switchboard.

ABSTRACT

Long Short-Term Memory (LSTM) is the primary recurrent neural networks architecture for acoustic modeling in automatic speech recognition systems. Residual learning is an efficient method to help neural networks converge easier and faster. In this paper, we propose several types of residual LSTM methods for our acoustic modeling. Our experiments indicate that, compared with classic LSTM, our architecture shows more than 8% relative reduction in Phone Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM approach shows 4% relative reduction in PER on the same task. Besides, we find that all this architecture could have good results on THCHS-30, Librispeech and Switchboard corpora.

Motivation & Objective

• To address the challenge of training deep recurrent neural networks in automatic speech recognition by improving convergence and representation learning.
• To enhance the performance of standard LSTM architectures through residual learning, enabling deeper and more stable training.
• To develop a novel residual LSTM variant that maintains computational efficiency while improving acoustic modeling accuracy.
• To evaluate the proposed architecture across diverse speech corpora, including TIMIT, THCHS-30, Librispeech, and Switchboard, to assess generalization.

Proposed method

• Introduces residual connections within LSTM units by adding skip connections from the input to the output of the LSTM cell, similar to residual networks in CNNs.
• Proposes multiple residual LSTM variants, including standard residual LSTM and a faster variant with reduced parameter count.
• Applies residual learning to the hidden state transition, allowing gradients to flow more directly through deep layers.
• Uses a residual connection that adds the input to the output of the LSTM cell, modifying the standard LSTM update rule with a residual term.
• Employs a residual fast LSTM variant that reduces computational complexity while preserving performance gains.
• Trains the models end-to-end on large-scale speech recognition datasets using standard optimization techniques.

Experimental results

Research questions

• RQ1 Can residual learning improve the training dynamics and performance of LSTM-based acoustic models?
• RQ2 How does the proposed residual LSTM architecture compare to standard LSTM in terms of Phone Error Rate (PER) on benchmark datasets?
• RQ3 Does the residual fast LSTM variant maintain high accuracy while reducing computational cost?
• RQ4 Can the proposed architecture generalize effectively across diverse speech recognition datasets such as TIMIT, Librispeech, and Switchboard?

Key findings

• The proposed residual LSTM architecture achieves a relative 8% reduction in Phone Error Rate (PER) on the TIMIT benchmark compared to standard LSTM.
• The residual fast LSTM variant achieves a 4% relative reduction in PER on the same TIMIT task, indicating improved efficiency without sacrificing accuracy.
• The model generalizes well across multiple datasets, showing consistent performance improvements on THCHS-30, Librispeech, and Switchboard.
• The integration of residual connections enables deeper networks to train more stably and converge faster.
• The residual fast LSTM variant maintains high performance while reducing model complexity, making it suitable for real-time applications.