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Although recent advances in deep learning technology improved automatic speech recognition (ASR), it remains difficult to recognize speech when it overlaps other people's voices. Speech separation or extraction is often used as a front-end to ASR to handle such overlapping speech. However, deep neural network-based speech enhancement can generate 'processing artifacts' as a side effect of the enhancement, which degrades ASR performance. For example, it is well known that single-channel noise reduction for non-speech noise (nonoverlapping speech) often does not improve ASR. Likewise, the processing artifacts may also be detrimental to ASR in some conditions when processing overlapping speech with a separation/extraction method, although it is usually believed that separation/extraction improves ASR. In order to answer the question 'Do we always have to separate/extract speech from mixtures?', we analyze ASR performance on observed and enhanced speech at various noise and interference conditions, and show that speech enhancement degrades ASR under some conditions even for overlapping speech. Based on these findings, we propose a simple switching algorithm between observed and enhanced speech based on the estimated signal-to-interference ratio and signal-to-noise ratio. We demonstrated experimentally that such a simple switching mechanism can improve recognition performance when processing artifacts are detrimental to ASR.

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Acoustic-to-word speech recognition based on attention-based encoder-decoder models achieves better accuracies with much lower latency than the conventional speech recognition systems. However, acoustic-to-word models require a very large amount of training data and it is difficult to prepare one for a new domain such as elderly speech. To address the problem, we propose domain adaptation based on transfer learning with layer freezing. Layer freezing first pre-trains a network with the source domain data, and then a part of parameters is retrained for the target domain while the rest is fixed. In the attentionbased acoustic-to-word model, the encoder part is frozen to maintain the generality, and only the decoder part is retrained to adapt to the target domain. This substantially allows for adaptation of the latent linguistic capability of the decoder to the target domain. Using a large-scale Japanese spontaneous speech corpus as source, the proposed method is applied to three target domains: a call center task and two voice search tasks by adults and by elderly. The models trained with the proposed method achieved better accuracy than the baseline models, which are trained from scratch or entirely retrained with the target domain.