29 citations · 87 across the 13 of their papers we have counts for
14 papers
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model
Apoorv Vyas, Srikanth Madikeri, Hervé Bourlard
In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduc…
Lattice-Free MMI Adaptation Of Self-Supervised Pretrained Acoustic Models
Apoorv Vyas, Srikanth Madikeri, Hervé Bourlard
In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model. We pretrain a Transformer model on thousand hours of untra…
Automatic dysarthric speech detection exploiting pairwise distance-based convolutional neural networks
P. Janbakhshi, I. Kodrasi, H. Bourlard
Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we p…
Automatic and perceptual discrimination between dysarthria, apraxia of speech, and neurotypical speech
I. Kodrasi, M. Pernon, M. Laganaro +1
Automatic techniques in the context of motor speech disorders (MSDs) are typically two-class techniques aiming to discriminate between dysarthria and neurotypical speech or between…
Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models
Srikanth Madikeri, Sibo Tong, Juan Zuluaga-Gomez +3
We present a simple wrapper that is useful to train acoustic models in PyTorch using Kaldi's LF-MMI training framework. The wrapper, called pkwrap (short form of PyTorch kaldi wrap…
Neural Network based End-to-End Query by Example Spoken Term Detection
Dhananjay Ram, Lesly Miculicich, Hervé Bourlard
This paper focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State-of-the-art approaches primarily rely on dynamic time warping…