Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

Abstract: Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the state-of-the-art in various benchmarks. Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid settings. However, RNNs are computationally expensive and sometimes difficult to train. In this paper, inspired by the advantages of both CNNs and the CTC approach, we propose an end-to-end speech framework for sequence labeling, by combining hierarchical CNNs with CTC directly without recurrent connections. By evaluating the approach on the TIMIT phoneme recognition task, we show that the proposed model is not only computationally efficient, but also competitive with the existing baseline systems. Moreover, we argue that CNNs have the capability to model temporal correlations with appropriate context information.

• The paper presents a novel CNN architecture integrated with CTC to perform end-to-end speech recognition without relying on RNNs.
• It achieves an 18.2% phoneme error rate on TIMIT and reduces training time by approximately 2.5 times compared to traditional models.
• The study demonstrates that appropriately configured CNNs can effectively model temporal dependencies, paving the way for scalable ASR systems.

Toward End-to-End Speech Recognition with Deep Convolutional Neural Networks

The paper presents a novel approach to automatic speech recognition (ASR), proposing a framework that combines Convolutional Neural Networks (CNNs) with Connectionist Temporal Classification (CTC) to accomplish end-to-end speech recognition without relying on recurrent neural networks (RNNs). This work builds on the established effectiveness of CNNs in handling spectral variabilities and modeling spectral correlations, while addressing the computational drawbacks posed by RNNs, particularly in terms of training speed and gradient-related issues.

Key Contributions and Methodology

1. A Novel Network Design: The authors introduce a deep CNN architecture capable of modeling temporal dependencies in speech recognition. This architecture leverages hierarchical stacked convolutional layers, allowing it to effectively replace traditional recurrent units in sequence labeling tasks. The CNN in this framework uses small filter sizes in the frequency domain to capture fine-grained spectral features and temporal correlations, thus alleviating the need for recurrent connections.
2. End-to-End Learning Framework: The integration of CNNs with CTC facilitates direct mapping from acoustic input to sequences of phonemes. The authors redefine a previously fragmented multi-step pipeline into a unified end-to-end trainable system. The advantage of CTC in this framework is its ability to handle variable-length outputs without the need for pre-segmented input data.
3. Efficient Training: The paper emphasizes the computational advantages of CNNs over RNNs, demonstrating a significantly faster training process while retaining strong performance metrics. This is poised to influence the scalability of such models to larger datasets significantly, though this paper only presents results for the TIMIT phoneme recognition task.
4. Evaluation and Results: The proposed model was evaluated on the TIMIT dataset, where it achieved a phoneme error rate (PER) of 18.2%. The performance is competitive, if not superior, to models using bi-directional LSTMs and other deep neural network architectures, while requiring approximately 2.5 times less training time.
5. Insights into CNN Capabilities: The research contributes insights into CNNs' ability to model temporal dependencies traditionally thought to necessitate the use of recurrent architectures. The appropriate configuration of CNNs, such as the number of layers and filter sizes, can effectively capture long-range dependencies.

Implications and Future Directions

The implications of this research are substantial for the field of ASR; moving away from RNN-centric models challenges prevailing assumptions about neural network architecture requirements for speech processing tasks. The demonstrated efficiency in training time makes CNNs particularly appealing for large-scale ASR systems, which could lead to more scalable and resource-efficient solutions.

For future work, training on larger vocabulary datasets and integrating LLMs with the proposed architecture are promising areas for exploration. Additionally, optimizations such as Batch Normalization could further enhance model performance and stability.

This paper opens avenues for reconsidering the architecture choices in ASR systems, potentially influencing new studies on optimizing CNN configurations for different domains and data scales. The shift toward end-to-end systems that leverage the computational strengths of CNNs could redefine benchmarks in speech recognition technology.