We address a low-performance problem of the elderly in automatic speech recognition (ASR) through feature adaptation agnostic to the ASR. Most of the datasets for speech recognition models consist of datasets collected from adult speakers. Consequently, the majority of commercial speech recognition systems typically tend to perform well on adult speakers. In other words, the limited diversity of speakers in the training datasets yields unreliable performance for minority (e.g., elderly) speakers due to the infeasible acquisition of training data. In response, this paper suggests a neural network-based voice conversion framework to enhance speech recognition of the minority. To this end, we propose a voice translation model including an unsupervised phonology clustering to extract linguistic information to fit the minority's speech to a current acoustic model frame. Our proposal is a spectral feature adaptation method that can be placed in front of any commercial or open ASR system, avoiding directly modifying the speech recognizer. The experimental results and analysis demonstrate the effectiveness of our proposed method through improvement in elderly speech recognition accuracy.

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Successful applications of deep learning technologies in the natural language processing domain have improved text-based intent classifications. However, in practical spoken dialogue applications, the users’ articulation styles and background noises cause automatic speech recognition (ASR) errors, and these may lead language models to misclassify users’ intents. To overcome the limited performance of the intent classification task in the spoken dialogue system, we propose a novel approach that jointly uses both recognized text obtained by the ASR model and a given labeled text. In the evaluation phase, only the fine-tuned recognized language model (RLM) is used. The experimental results show that the proposed scheme is effective at classifying intents in the spoken dialogue system containing ASR errors.