End-to-end attention-based distant speech recognition with Highway LSTM
End-to-end attention-based models have been shown to be competitive alternatives to conventional DNN-HMM models in the Speech Recognition Systems. In this paper, we extend existing end-to-end attention-based models that can be applied for Distant Speech Recognition (DSR) task. Specifically, we propose an end-to-end attention-based speech recognizer with multichannel input that performs sequence prediction directly at the character level. To gain a better performance, we also incorporate Highway long short-term memory (HLSTM) which outperforms previous models on AMI distant speech recognition task.
Code (0)
등록된 구현이 없습니다.
Tasks
Distant Speech Recognitionspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Highway Long Short-Term Memory RNNs for Distant Speech Recognition
In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, …
Distant Speech Recognitionspeech-recognitionSpeech RecognitionResidual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition
In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a …
Distant Speech Recognitionspeech-recognitionSpeech RecognitionLanguage Modeling with Highway LSTM
Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks. In this paper, we extend an LSTM by adding highway networks inside an LSTM and use the result…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+2Quaternion Neural Networks for Multi-channel Distant Speech Recognition
Despite the significant progress in automatic speech recognition (ASR), distant ASR remains challenging due to noise and reverberation. A common approach to mitigate this issue consists of equipping the recording devices…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Distant Speech Recognitionspeech-recognition+1Semi-tied Units for Efficient Gating in LSTM and Highway Networks
Gating is a key technique used for integrating information from multiple sources by long short-term memory (LSTM) models and has recently also been applied to other models such as the highway network. Although gating is …
speech-recognitionSpeech Recognition