Title: Letter-Based Speech Recognition with Gated ConvNets,
Abstract: In the recent literature, "end-to-end" speech systems often refer to
letter-based acoustic models trained in a sequence-to-sequence manner, either
via a recurrent model or via a structured output learning approach (such as
CTC). In contrast to traditional phone (or senone)-based approaches, these
"end-to-end'' approaches alleviate the need of word pronunciation modeling, and
do not require a "forced alignment" step at training time. Phone-based
approaches remain however state of the art on classical benchmarks. In this
paper, we propose a letter-based speech recognition system, leveraging a
ConvNet acoustic model. Key ingredients of the ConvNet are Gated Linear Units
and high dropout. The ConvNet is trained to map audio sequences to their
corresponding letter transcriptions, either via a classical CTC approach, or
via a recent variant called ASG. Coupled with a simple decoder at inference
time, our system matches the best existing letter-based systems on WSJ (in word
error rate), and shows near state of the art performance on LibriSpeech.