End-to-end automatic speech recognition (ASR) has simplified the traditional ASR system building pipeline by eliminating the need to have multiple components and also the requirement for expert linguistic knowledge for creating pronunciation dictionaries. Therefore, end-to-end ASR fits well when building systems for new domains. However, one major drawback of end-to-end ASR is that, it is necessary to have a larger amount of labeled speech in comparison to traditional methods. Therefore, in this paper, we explore domain adaptation approaches for end-to-end ASR in low-resource settings. We show that joint domain identification and speech recognition by inserting a symbol for domain at the beginning of the label sequence, factorized hidden layer adaptation and a domain-specific gating mechanism improve the performance for a low-resource target domain. Furthermore, we also show the robustness of proposed adaptation methods to an unseen domain, when only 3 hours of untranscribed data is available with improvements reporting up to 8.7% relative.
State-of-the-art automatic speech recognition (ASR) systems use sequence discriminative training for improved performance over frame-level cross-entropy (CE) criterion. Even though sequence discriminative training improves long short-term memory (LSTM) recurrent neural network (RNN) acoustic models (AMs), it is not clear whether these systems achieve the optimal performance due to overfitting. This paper investigates the effect of state-level minimum Bayes risk (sMBR) training on LSTM AMs and shows that the conventional way of performing sMBR by updating all LSTM parameters is not optimal. We investigate two methods to improve the performance of sequence discriminative training of LSTM AMs. First more feed-forward (FF) layers are included between the last LSTM layer and the output layer so those additional FF layers may bene- fit more from sMBR training. Second, a subspace is estimated as an interpolation of rank-1 matrices when performing sMBR for the LSTM layers of the AM. Our methods are evaluated in benchmark AMI single distance microphone (SDM) task. We find that the proposed approaches provide 1.6% absolute improvement over a strong sMBR trained LSTM baseline.