Very Deep Convolutional Networks for End-to-End Speech Recognition (1610.03022v1)

Abstract: Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and convolutional LSTMs to build very deep recurrent and convolutional structures. Our models exploit the spectral structure in the feature space and add computational depth without overfitting issues. We experiment with the WSJ ASR task and achieve 10.5\% word error rate without any dictionary or language using a 15 layer deep network.

• The paper introduces deep convolutional and recurrent structures within seq2seq models, using NiN modules and residual connections for enhanced capacity.
• The integration of batch normalization and ConvLSTM significantly accelerates training and mitigates overfitting, leading to a 10.5% WER on the WSJ ASR task.
• The study demonstrates that adapting vision-inspired deep learning techniques to speech recognition can drive impactful improvements and interdisciplinary innovation.

Exploring Deep Convolutional Networks for Speech Recognition

The paper "Very Deep Convolutional Networks for End-to-End Speech Recognition" presents a significant advancement in automatic speech recognition (ASR) models by integrating principles from deep convolutional networks previously successful in computer vision. The authors introduce the application of deep convolutional and recurrent structures in sequence-to-sequence (seq2seq) models, resulting in improvements in both expressive power and generalization for ASR tasks.

Methodological Contributions

The paper systematically explores several architectural innovations designed to extend the depth of seq2seq ASR models without succumbing to overfitting:

1. Network-in-Network (NiN) Modules: The researchers leverage 1x1 convolutions to increase network depth, thereby enhancing the model's expressive capacity while maintaining a manageable number of parameters. These modules are integrated into the hierarchical subsampling connections between Long Short-Term Memory (LSTM) layers.
2. Batch Normalization (BN): By applying BN across layers, the authors mitigate internal covariate shifts, which serves to accelerate training and improve model regularization. This step was crucial in training deeper models successfully.
3. Residual Connections: The paper incorporates residual networks (ResNets) to enable the training of robust, very deep networks. These connections combat the potential pitfalls of deep architectures, such as poor optimization and generalization issues.
4. Convolutional LSTM (ConvLSTM): By substituting inner products in LSTM units with convolutions, ConvLSTMs preserve structural locality in the cell states and outputs, enhancing model capacity while reducing overfitting incidents.

Performance and Results

The model was rigorously evaluated using the Wall Street Journal (WSJ) ASR task, achieving a word error rate (WER) of 10.5% using a 15-layer deep network without external dictionaries or LLMs. This performance marks a notable improvement over previous published results, evidencing a 4.26% absolute gain in WER compared to the baseline models assessed by prior research. The integration of convolutional layers and residual connections proved particularly effective, as shown by detailed experimental comparisons.

Practical and Theoretical Implications

This work underscores the viability of employing advanced deep learning architectures in the ASR domain, which may prompt further exploration into deep network designs for sequence modeling tasks. The demonstrated improvements in WER showcase the potential for applying similar methodologies to other domains relying on seq2seq frameworks. Moreover, this integration of vision-inspired techniques into speech recognition tasks champions a broader interdisciplinary approach that could extend to additional areas such as natural language processing and audio processing.

Speculation on Future Advances

As the research indicates, increasing the depth and non-linearity of seq2seq models offers promising advancements in model performance. Future explorations may delve into hybrid approaches combining various deep learning paradigms or sophisticated training techniques like reinforcement learning. Additionally, the utilization of advanced hardware and optimization strategies could facilitate the deployment of even deeper models, opening up new avenues for improving ASR systems and expanding their capabilities in challenging environments and diverse applications.

In sum, this paper provides a compelling demonstration of the effectiveness of very deep convolutional networks integrated with traditional seq2seq models for ASR, setting a foundation for continued innovations in the field.