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This paper describes the team (“ODI-ANLP”)’s submission to WAT 2020. We have participated in the English→HindiMultimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and Incep-tionResNetV2 for the Hindi ...
In this paper, we explore various approaches for semi-
supervised learning in an end-to-end automatic speech recog-
nition (ASR) framework. The first step in our approach in-
volves training a seed model on the limited amount of labelled
data. Additional u ...
In air traffic control rooms, paper flight strips are more and more replaced by digital solutions. The digital systems, however, increase the workload for air traffic controllers: For instance, each voice-command must be manually inserted into the system b ...
Idiap has made a submission to the conversational telephony speech (CTS) challenge of the NIST SRE 2019. The submission consists of six speaker verification (SV) systems: four extended TDNN (E-TDNN) and two TDNN x-vector systems. Employment of various trai ...
In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent ( among many others) are suppress ...
In this paper, we describe the participation of the Idiap Research Institute at GermEval 2020 shared task on the Classification and Regression of Cognitive and Motivational style from Text, specifically on subtask 2, Classification of the Operant Motive Te ...
Although current trends in speech processing consider deep learning through data-driven technologies, many potential applications exhibit lack of training or development data. Therefore, considerably light signal processing techniques are still of interest ...
In this paper, we introduce a novel approach for Language Identification (LID). Two commonly used state-of-the-art methods based on UBM/GMM I-vector technique, combined with a back-end classifier, are first evaluated. The differential factor between these ...
In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent ( among many others) are suppress ...
Despite the recent success of deep neural network-based approaches in sound source localization, these approaches suffer the limitations that the required annotation process is costly, and the mismatch between the training and test conditions undermines th ...
