Transformers with convolutional context for ASR (1904.11660v2)

Abstract: The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition. Recent efforts studied key research questions around ways of combining positional embedding with speech features, and stability of optimization for large scale learning of transformer networks. In this paper, we propose replacing the sinusoidal positional embedding for transformers with convolutionally learned input representations. These contextual representations provide subsequent transformer blocks with relative positional information needed for discovering long-range relationships between local concepts. The proposed system has favorable optimization characteristics where our reported results are produced with fixed learning rate of 1.0 and no warmup steps. The proposed model achieves a competitive 4.7% and 12.9% WER on the Librispeech test clean'' andtest other'' subsets when no extra LM text is provided.

Transformers with Convolutional Context for ASR

The presented paper explores an innovative approach to Automatic Speech Recognition (ASR) through the integration of convolutional context within transformer networks. The authors propose substituting sinusoidal positional embeddings with convolutionally learned input representations that significantly enhance the ability of transformer models to discern long-range dependencies between local concepts. The primary focus is on optimizing the ASR task by infusing convolutional features early in the processing pipeline, thereby simplifying transformer optimization and enhancing performance stability.

Proposed Architecture and Methodology

The authors divide the modeling task into two distinct components: convolutional layers for capturing local relationships and transformer layers for modeling global sequential structures. By using convolutional layers to extract positional information, they provide transformer layers with a stable representation upon which long-range relationships are more readily discerned. This structure benefits from fixed learning rate conditions without necessitating warm-up steps, thus contributing to stable optimization conditions.

The model architecture consists of 2-D convolutional blocks within the encoder and 1-D convolutional layers within the decoder, facilitating thorough extraction and application of contextual information. The encoder's deeper structure is critical in achieving refined representations of speech features that marginalize extraneous speaker and environment characteristics, thereby enhancing focus on content.

Experimental Results

Regarding empirical assessment, the paper reports a notable performance on the Librispeech dataset by demonstrating competitive Word Error Rates (WER) of 4.7% and 12.9% on the test clean" andtest other" subsets, respectively. These metrics are achieved without leveraging auxiliary LLMs, showcasing the efficacy of convolutional context integration. The experiments validate that convolutional positional information can satisfactorily replace sinusoidal embeddings, promoting efficient learning of global word orders and robust speaker/environment characteristic modeling.

The experiments further elucidate the impact of different architectural decisions, particularly concerning convolutional depth and context size. For instance, increased depth in the encoder proves crucial for optimizing WER by effectively modeling long-range structural data and speaker/environment nuances. This approach garners a relative reduction of 12% to 16% in WER compared to previous methodologies on acoustically challenging subsets, emphasizing the performance gains attributable to convolutional context.

Implications and Future Directions

The findings presented offer substantial implications for both practical ASR system designs and future theoretical advancements. The paper's results support the proposition that convolutional augmentation in transformers can improve positional encoding and long-range sequence modeling without additional LLM training on extra textual data. Such insights could streamline ASR systems targeting resource-efficient deployments.

The authors identify potential avenues for future research, particularly the combined use of this architecture with advanced training protocols such as Optimal Completion Distillation (OCD). Future exploration in this direction may yield further refinements in ASR system performance, accentuating the utility of convolutional context in neural architectures.

In summary, this paper offers substantive contributions to ASR technologies by illustrating the potency of convolutional context in transformer networks, optimizing positional information extraction, and presenting a novel pathway for efficient sequence modeling in speech recognition tasks.