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This paper advances the design of CTC-based all-neural (or end-toend) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks.We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology.

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In this work, we propose two improvements to attention based sequence-to-sequence models for end-to-end speech recognition systems. For the first improvement, we propose to use an input-feeding architecture which feeds not only the previous context vector but also the previous decoder hidden state information as inputs to the decoder. The second improvement is based on a better hypothesis generation scheme for sequential minimum Bayes risk (MBR) training of sequence-to-sequence models where we introduce softmax smoothing into N-best generation during MBR training. We conduct the experiments on both Switchboard-300hrs and Switchboard+Fisher-2000hrs datasets and observe significant gains from both proposed improvements. Together with other training strategies such as dropout and scheduled sampling, our best model achieved WERs of 8.3%/15.5% on the Switchboard/CallHome subsets of Eval2000 without any external language models which is highly competitive among state-of-the-art English conversational speech recognition systems. Index Terms: attention based sequence-to-sequence models, end-to-end speech recognition, sequential minimum Bayes risk training, MBR

In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR training is conducted via minimizing the expected edit distance between the reference label sequence and on-thefly generated N-best hypothesis. We also introduce a heuristic to incorporate an external neural network language model (NNLM) in RNN-T beam search decoding and explore MBR training with the external NNLM. Experimental results demonstrate an MBR trained model outperforms a RNN-T trained model substantially and further improvements can be achieved if trained with an external NNLM. Our best MBR trained system achieves absolute character error rate (CER) reductions of 1.2% and 0.5% on read and spontaneous Mandarin speech respectively over a strong convolution and transformer based RNN-T baseline trained on ∼21,000 hours of speech.

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