Improving End-to-End Speech Recognition with Deep Neural Beamforming

In this groundbreaking paper, Vaswani et al. propose a novel approach to automatic speech recognition (ASR) called end-to-end neural beamforming. Unlike traditional hybrid systems that combine DNNs and HMMs, the proposed system relies solely on deep neural networks (DNNs) to perform both acoustic modeling and language modeling. This end-to-end approach simplifies the system architecture and improves performance.
To address the issue of sharing an objective between all components in a single system, the authors introduce the concept of attention mechanisms. Attention allows the system to focus on specific parts of the input sequence, much like how we selectively listen to different people in a noisy room. By applying this mechanism at multiple scales, the system can capture both local and global context, enabling it to better understand complex speech patterns.
The proposed end-to0end system consists of an encoder-decoder architecture, where the encoder generates a sequence of hidden states that are passed to the decoder to produce the output transcript. The attention mechanism is applied at multiple scales within each layer of the decoder, allowing the system to selectively focus on different parts of the input sequence as it processes them.
The authors evaluate their proposed approach using several benchmark datasets and show that it significantly outperforms traditional hybrid systems in terms of both accuracy and computational efficiency. They also demonstrate the effectiveness of their attention mechanism by analyzing the attention weights generated during decoding, revealing how the system selectively focuses on different parts of the input sequence as it processes them.
In summary, "Attention is all you need" presents a groundbreaking end-to-end neural beamforming approach to ASR that simplifies the system architecture and improves performance by introducing attention mechanisms. This work has far-reaching implications for the field of speech recognition and may pave the way for even more advanced systems in the future.