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In automatic speech recognition (ASR), error correction after the initial search stage is a commonly used technique to improve performance. Whilst completely automatic error correction, such as full second pass rescoring using complex language models, is widely used, directed error correction, where the error locations are manually given, is of great interest in many scenarios. Previous works on directed error correction usually uses the error location information to change search space with original ASR models.

In the present study, the ultrasonic data of two prelingual deaf participants were collected to observe tongue movements during the production of all the apical syllables under four citation tones except for \emph{ri} in Mandarin Chinese. Results of data analysis showed that, besides their personal characteristics, the two participants share similar problems in producing those apical syllables such as producing alveolar syllables as post-alveolar syllables, realizing affricates as fricatives, and unable to pronounce some types of apical syllables which they can perceive correctly.

This paper compared the singing voices of four student singers of Chinese national singing before and after vocal warm-up. Statistics showed that the parameters such as deviation from the standard note, vibrato rate and jitter were undergoing significant changes after 30 minutes of warming up exercise, while the differences of vibrato extent demonstrated a controversy result.

This paper compared the singing voices of four student singers of Chinese national singing before and after vocal warm-up. Statistics showed that the parameters such as deviation from the standard note, vibrato rate and jitter were undergoing significant changes after 30 minutes of warming up exercise, while the differences of vibrato extent demonstrated a controversy result.

Although uni-directional recurrent neural network language
model(RNNLM) has been very successful, it’s hard to train a
bi-directional RNNLM properly due to the generative nature of
language model. In this work, we propose to train bi-directional
RNNLM with noise contrastive estimation(NCE), since the
properities of NCE training will help the model to acheieve
sentence-level normalization. Experiments are conducted on
two hand-crafted tasks on the PTB data set: a rescore task and

In process of learning Chinese as a second language (CSL), Japanese natives have difficulties in tone perception. Among the four Chinese lexical tones, the tone pairs Tone 1-Tone 2 and Tone 1-Tone 4 are problematic for Japanese CSL beginners. In order to help them develop efficiently discriminating capability of the tone pairs, we designed a hybrid perceptual training scheme which combined adaptive training and high variability phonetic training.