Connectionist Temporal Classification

AbstractEnd-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, …...

Transcription of speech is an important part of language documentation, and yet speech recognition technology has not been widely harnessed to aid linguists. We explore the use of a neural …...

Transcribing speech is an important part of language documentation, yet speech recognition technology has not been widely harnessed to aid linguists. We explore the use of a neural network architecture …...

The recent emergence of end-to-end automatic speech recognition (ASR) frameworks has streamlined the complicated modeling procedures of ASR systems in contrast to the conventional deep neural network-hidden Markov (DNN-HMM) ASR …...

Communication is an important part of life. To use communication technology efficiently we need to know how to use them or how to instruct these devices to perform tasks. Automatic …...

AbstractPrevious studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). However, they required a dedicated model for phone recognition and did not …...

In this paper, we describe the system team NSYSU-MITLab implemented for Formosa Speech Recognition Challenge 2020. We use the Transformer architecture  國立中山大學資訊工程學系 Department of Computer Science and Information Engineering …...

AbstractWe present a non-autoregressive system submission to the WMT 22 Efficient Translation Shared Task. Our system was used by Helcl et al. (2022) in an attempt to provide fair comparison …...

AbstractCombining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency. In this paper, we …...