Search Results for author:
Found 10 papers, 6 papers with code
Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews.
Implementing contextual biasing in GPU decoder for online ASR
GPU decoding significantly accelerates the output of ASR predictions.
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding
This paper explores the lessons learned from the ATCO2 project, a project that aimed to develop a unique platform to collect and preprocess large amounts of ATC data from airspace in real time.
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks
In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
A Comparison of Methods for OOV-word Recognition on a New Public Dataset
A common problem for automatic speech recognition systems is how to recognize words that they did not see during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model
On Switchboard (300h) we obtain relative improvements of 33% and 35% respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Lattice-Free MMI Adaptation Of Self-Supervised Pretrained Acoustic Models
In this work, we propose lattice-free MMI (LFMMI) for supervised adaptation of self-supervised pretrained acoustic model.
Pkwrap: a PyTorch Package for LF-MMI Training of Acoustic Models
We present a simple wrapper that is useful to train acoustic models in PyTorch using Kaldi's LF-MMI training framework.
Audio and Speech Processing Sound
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage
In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains
In this study, we propose a multilingual neural language model architecture, trained jointly on the domain-specific data of several low-resource languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
