Spoken language identification refers to the task of automatically predi...
In the recent years, speech representation learning is constructed prima...
The representation learning of speech, without textual resources, is an ...
Speech representation learning approaches for non-semantic tasks such as...
This paper presents the Coswara dataset, a dataset containing diverse se...
Emotion recognition in conversations is challenging due to the multi-mod...
Conventional methods for speaker diarization involve windowing an audio ...
In this paper, we propose a model to perform style transfer of speech to...
In this paper, we describe an approach for representation learning of au...
The COVID-19 outbreak resulted in multiple waves of infections that have...
This report describes the system used for detecting COVID-19 positives u...
The COVID-19 pandemic has accelerated research on design of alternative,...
The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aime...
In this paper, we propose a novel algorithm for speaker diarization usin...
The task of speech recognition in far-field environments is adversely
af...
In this work, we propose a multi-head relevance weighting framework to l...
This paper presents the details of the SRIB-LEAP submission to the
Confe...
The technology development for point-of-care tests (POCTs) targeting
res...
The research direction of identifying acoustic bio-markers of respirator...
The electroencephalography (EEG), which is one of the easiest modes of
r...
Automatic speaker diarization techniques typically involve a two-stage
p...
The DiCOVA challenge aims at accelerating research in diagnosing COVID-1...
The normalization of brain recordings from multiple subjects responding ...
Automatic transcription of monophonic/polyphonic music is a challenging ...
This paper introduces the third DIHARD challenge, the third in a series ...
The learning of interpretable representations from raw data presents
sig...
Speech recognition in noisy and channel distorted scenarios is often
cha...
While deep learning models have made significant advances in supervised
...
Automatic speech recognition in reverberant conditions is a challenging ...
Many commercial and forensic applications of speech demand the extractio...
This paper introduces the third DIHARD challenge, the third in a series ...
The COVID-19 pandemic presents global challenges transcending boundaries...
The task of automatic language identification (LID) involving multiple
d...
The state-of-art approach for speaker verification consists of a neural
...
The NIST Speaker Recognition Evaluation - Conversational Telephone Speec...
The state-of-art approach to speaker verification involves the extractio...
Despite recent advances in voice separation methods, many challenges rem...
The state-of-art methods for acoustic beamforming in multi-channel ASR a...
Automatic speech recognition in multi-channel reverberant conditions is ...
This paper introduces the second DIHARD challenge, the second in a serie...
The problem of automatic accent identification is important for several
...
We present the recent advances along with an error analysis of the IBM
s...
In this paper we describe the recent advancements made in the IBM i-vect...