We present a simple and efficient auxiliary loss function for automatic speech recognition
(ASR) based on the connectionist temporal classification (CTC) objective. The proposed …

Transformers have recently dominated the ASR field. Although able to yield good
performance, they involve an autoregressive (AR) decoder to generate tokens one by one …

This paper introduces FunASR, an open-source speech recognition toolkit designed to
bridge the gap between academic research and industrial applications. FunASR offers …

This exploration aims to study the emotion recognition of speech and graphic visualization of
expressions of learners under the intelligent learning environment of the Internet. After …

Unifying acoustic and linguistic representation learning has become increasingly crucial to
transfer the knowledge learned on the abundance of high-resource language data for low …

Transformer-based models have led to significant innovation in various classic and practical
subjects, including speech processing, natural language processing, and computer vision …

Respiratory sound classification is an important tool for remote screening of respiratory-
related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretability …

Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more
and more attention. Many efforts have been paid to turn the non-streaming attention-based …

Long utterances segmentation is crucial in end-to-end (E2E) streaming automatic speech
recognition (ASR). However, commonly used voice activity detection (VAD)-based and fixed …

Recently, end-to-end (E2E) speech recognition has become popular, since it can integrate
the acoustic, pronunciation and language models into a single neural network, which …