Phonetic Temporal Neural Model for Language Identification
Chengdu Institute of Computer Applications, Chinese Academy of Sciences（中国科学院成都计算机应用研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
Zhiyuan Tang, Dong Wang, Yixiang Chen, Lantian Li, Andrew Abel
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.
BERT-LID: Leveraging BERT to Improve Spoken Language Identification
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Computational Media Innovation Centre（计算媒体创新中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；TAL Education（好未来教育集团）
Language Identification with Deep Bottleneck Features
Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications（北京邮电大学模式识别与智能系统实验室）
提出了一种基于长短期记忆（Long Short Term Memory，LSTM）神经网络的端到端短话语语音语言识别（SLD）方法，该方法特别适用于智能车辆中的SLD应用。用于LSTM学习的特征由迁移学习方法生成。利用深度神经网络（DNN）的瓶颈特征对普通话语音进行分类，并将其用于LSTM训练。为了提高短话语的SLD精度，采用了基于相位声码器的时间尺度修正（TSM）方法来降低和提高测试话语的语音等级。通过对正常语音、降低速率语音和提高速率语音进行拼接，可以延长测试语音的长度，从而提高SLD系统的性能。在AP 17-OLR语料库上的实验结果表明，本文提出的方法能够有效地提高SLD的性能，尤其是对时长为1 s和3 s的短话语的识别效果更好。
Temporal Consistency for LLM Reasoning Process Error Identification
Department of Electrical & Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；AI Lab, Princeton University（普林斯顿大学人工智能实验室）；Department of Computer Science & Engineering, University of Michigan（密歇根大学计算机科学与工程系）
