8 accepted papers
We present a lightweight neural network with attentive score loss for frame-wise personalized voice activity detection (i.e., AS-pVAD). Instead of using an external speaker embedding extractor with a large number of parameters, AS-pVAD employs a lightweight internal model to extract the target speak…
This paper presents a deep and lightweight subband neural network which jointly suppresses the common interference in real-time fullband speech communication: echo, noise and reverberation. Preserving the advantages of spectro-temporal subband network (STSubNet) that requires small amount of resourc…
There has been much recent interest in building continuous speech recognition systems for people with severe speech impairments, e.g., dysarthria. However, the datasets that are commonly used are typically designed for tasks other than ASR development, or they contain only isolated words. As such, t…
This paper presents an improved transfer learning framework applied to robust personalised speech recognition models for speakers with dysarthria. As the baseline of transfer learning, a state-of-the-art CNN-TDNN-F ASR acoustic model trained solely on source domain data is adapted onto the target do…
Improving the accuracy of personalised speech recognition for speakers with dysarthria is a challenging research field. In this paper, we explore an approach that non-linearly modifies speech tempo to reduce mismatch between typical and atypical speech. Speech tempo analysis at the phonetic level is…
A multi-stream framework with deep neural network (DNN) classifiers is applied to improve automatic speech recognition (ASR) in environments with different reverberation characteristics. We propose a room parameter estimation model to establish a reliable combination strategy which performs on eithe…
A multi-stream framework with deep neural network (DNN) classifiers has been applied in this paper to improve automatic speech recognition (ASR) performance in environments with different reverberation characteristics. We propose a room parameter estimation model to determine the stream weights for…
This work evaluates multi-microphone beamforming and single-microphone spectral enhancement strategies to alleviate the reverberation effect for robust automatic speech recognition (ASR) systems in different reverberant environments characterized by different reverberation times T60 and direct-to-re…