Efficient End-to-End Speech Recognition Using Performers in Conformers
On-device end-to-end speech recognition poses a high requirement on model efficiency. Most prior works improve the efficiency by reducing model sizes. We propose to reduce the complexity of model architectures in addition to model sizes. More specifically, we reduce the floating-point operations in conformer by replacing the transformer module with a performer. The proposed attention-based efficient end-to-end speech recognition model yields competitive performance on the LibriSpeech corpus with 10 millions of parameters and linear computation complexity. The proposed model also outperforms previous lightweight end-to-end models by about 20% relatively in word error rate.
Code (0)
등록된 구현이 없습니다.
Tasks
speech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Augmenting conformers with structured state-space sequence models for online speech recognition
Online speech recognition, where the model only accesses context to the left, is an important and challenging use case for ASR systems. In this work, we investigate augmenting neural encoders for online ASR by incorporat…
speech-recognitionSpeech RecognitionTowards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and its Applications
This article surveys convolution-based models including convolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recogni…
Emotion RecognitionSpeaker IdentificationSpeech Enhancementspeech-recognition+1On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments
Speech separation remains an important topic for multi-speaker technology researchers. Convolution augmented transformers (conformers) have performed well for many speech processing tasks but have been under-researched f…
Computational EfficiencySpeech SeparationEnd-to-end Audio-visual Speech Recognition with Conformers
In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner. In particular, the audio and visual encoders lear…
Audio-Visual Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+6Linear Time Complexity Conformers with SummaryMixing for Streaming Speech Recognition
Automatic speech recognition (ASR) with an encoder equipped with self-attention, whether streaming or non-streaming, takes quadratic time in the length of the speech utterance. This slows down training and decoding, incr…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition