8 citations · 15 across the 17 of their papers we have counts for
5 papers · 1 filter
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach
Kai Zhen, Martin Radfar, Hieu Duy Nguyen +3
For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency. Amon…
ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition
Martin Radfar, Rohit Barnwal, Rupak Vignesh Swaminathan +4
The recurrent neural network transducer (RNN-T) is a prominent streaming end-to-end (E2E) ASR technology. In RNN-T, the acoustic encoder commonly consists of stacks of LSTMs. Very…
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks
Aneesh Muppidi, Martin Radfar
Although speech recognition has become a widespread technology, inferring emotion from speech signals still remains a challenge. To address this problem, this paper proposes a quat…
The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input
Bradley He, Martin Radfar
In order to evaluate the performance of the attention based neural ASR under noisy conditions, the current trend is to present hours of various noisy speech data to the model and m…
Speech Separation Using Gain-Adapted Factorial Hidden Markov Models
Martin H. Radfar, Richard M. Dansereau, Willy Wong
We present a new probabilistic graphical model which generalizes factorial hidden Markov models (FHMM) for the problem of single-channel speech separation (SCSS) in which we wish t…