Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning (1609.06773v2)

Abstract: Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. One approach is the attention-based encoder-decoder framework that learns a mapping between variable-length input and output sequences in one step using a purely data-driven method. The attention model has often been shown to improve the performance over another end-to-end approach, the Connectionist Temporal Classification (CTC), mainly because it explicitly uses the history of the target character without any conditional independence assumptions. However, we observed that the performance of the attention has shown poor results in noisy condition and is hard to learn in the initial training stage with long input sequences. This is because the attention model is too flexible to predict proper alignments in such cases due to the lack of left-to-right constraints as used in CTC. This paper presents a novel method for end-to-end speech recognition to improve robustness and achieve fast convergence by using a joint CTC-attention model within the multi-task learning framework, thereby mitigating the alignment issue. An experiment on the WSJ and CHiME-4 tasks demonstrates its advantages over both the CTC and attention-based encoder-decoder baselines, showing 5.4-14.6% relative improvements in Character Error Rate (CER).

Joint CTC-Attention Based End-to-End Speech Recognition Using Multi-Task Learning

The paper "Joint CTC-Attention Based End-to-End Speech Recognition Using Multi-Task Learning" by Suyoun Kim, Takaaki Hori, and Shinji Watanabe, presents a novel approach to end-to-end speech recognition that leverages both Connectionist Temporal Classification (CTC) and attention mechanisms within a multi-task learning (MTL) framework. This hybrid model aims to address some of the limitations observed in individual CTC and attention-based models.

Summary and Key Contributions

End-to-end speech recognition has seen significant advancements, where models directly transcribe speech to text without requiring predefined alignments between acoustic frames and text characters. Traditional methods such as Deep Neural Networks - Hidden Markov Models (DNN-HMMs) involve separate training phases for different components, leading to potential suboptimal performance due to disjoint training procedures. End-to-end models seek to simplify this by learning mappings from acoustic frames to character sequences in a single step.

There are two predominant approaches in end-to-end speech recognition: CTC and attention-based encoder-decoder models. The CTC model employs intermediate label representations, allowing repetitions of labels and blank labels to manage the variance in input and output sequence lengths. However, the CTC approach assumes conditional independence among the outputs, which can limit its capacity for modeling long-range dependencies.

Conversely, attention-based encoder-decoder models directly learn mappings from input frames to output character sequences without any assumption of conditional independence. This approach often yields improved Character Error Rates (CER) in noiseless environments. However, attention models struggle with noisy data and long input sequences, as they can become easily misaligned due to their highly flexible nature.

The authors propose a hybrid model that integrates CTC and attention mechanisms using an MTL framework. The shared encoder is trained simultaneously by both CTC and attention objectives. By doing so, the model leverages the strengths of both approaches: the left-to-right constraints of CTC for robust alignment and the flexibility of attention mechanisms for capturing dependencies in the data.

Experimental Evaluation

The proposed joint CTC-attention model was evaluated on both clean speech corpora (WSJ1 and WSJ0) and a noisy speech corpus (CHiME-4). The results demonstrate significant improvements over both standalone CTC and attention models. Notably, the proposed model achieved relative improvements in CER ranging from 5.4% to 14.6% across different datasets.

The paper also addressed the learning efficiency of the models. The joint model showed faster convergence rates, attributed to the alignment constraints imposed by the CTC objective, which guides the learning process more effectively, especially in noisy conditions. Learning curves and alignment visualizations consistently showed that the joint model achieved desired alignments significantly faster than the attention model alone.

Implications and Future Work

The integration of CTC with attention mechanisms within an MTL framework offers several practical and theoretical advantages. Practically, the hybrid model demonstrates improved robustness and faster learning, critical for real-world applications where data may be noisy and abundant. Theoretically, the approach highlights the benefits of combining sequential labeling constraints with sequence-to-sequence learning, paving the way for future exploration in other sequence-based tasks.

In the future, this method can be extended and applied to other domains such as machine translation, where sequence-to-sequence mappings are also crucial. Further research could explore optimizing the balance between CTC and attention contributions, as indicated by the parameter $\lambda$, to generalize better across various datasets and conditions. Additionally, the impact of incorporating external LLMs with the proposed hybrid framework warrants further investigation.

In summary, the joint CTC-attention model via MTL represents a significant development in end-to-end speech recognition, combining the strengths of CTC and attention mechanisms to achieve robust and efficient learning.