Exploring neural transducers for end-to-end speech recognition

Citations Over TimeTop 1% of 2017 papers

Abstract

In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue — RNN-Transducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models — when all encoder layers are forward only, and when encoders downsample the input representation aggressively.

Related Papers

• → Does End-to-End Trained Deep Model Always Perform Better than Non-End-to-End Counterpart?(2021)2 cited
• → Fusion Recurrent Neural Network(2020)4 cited
• → A Study on Performance Improvement of Recurrent Neural Networks Algorithm using Word Group Expansion Technique(2022)