论文: SPEECH-TRANSFORMER: A NO-RECURRENCE SEQUENCE-TO-SEQUENCE MODELFOR SPEECH RECOGNITION ...
论文: Deep FSMN for Large Vocabulary Continuous Speech Recognition 思想: 对于大词汇量语音识别,往往需要更深的网络结构,但是当FSMN 或cFSMN 的结构很深时容易引发剃度消失和爆炸问题 于是本文对cFSMN结构进一步改进,对序列记忆模块之间引入skip connection,保证信息在更深的层之间传播,缓解剃度消失和爆炸的问题 ...
2020-09-16 21:51 0 863 推荐指数:
论文: SPEECH-TRANSFORMER: A NO-RECURRENCE SEQUENCE-TO-SEQUENCE MODELFOR SPEECH RECOGNITION ...
LAS: listen, attented and spell,Google 思想: sequence to sequence的思想,模型分为encoder和dec ...
论文: EESEN:END-TO-END SPEECH RECOGNITION USING DEEP RNN MODELS AND WFST-BASED DECODING ...
论文: CTC:Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks 思想: 语音识别中,一般包含语音 ...
的时序长度,在大规模语音数据训练时提升计算效率; 2)decoder输入采样策略,如果训练时 ...
论文: TRANSFORMER-TRANSDUCER:END-TO-END SPEECH RECOGNITION WITH SELF-ATTENTION 思想: 1)借助RNN-T在语音识别上的优势,通过tranformer替换RNN-T中的RNN结构,实现 ...
论文: A time delay neural network architecture for efficient modeling of longtemporal contexts ...
论文: TRANSFORMER TRANSDUCER: A STREAMABLE SPEECH RECOGNITION MODELWITH TRANSFORMER ENCODERS A ...