10 accepted papers
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by independently fusing the knowledge from the various modalities for performing emotion classification. In co…
In this work, we explore a data scheduling strategy for semi-supervised learning (SSL) for acoustic modeling in automatic speech recognition. The conventional approach uses a seed model trained with supervised data to automatically recognize the entire set of unlabeled (auxiliary) data to generate n…
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 suppressed. Inter-task fusion, in which such metadata…
In this paper, we are interested in exploiting textual and acoustic data of an utterance for the speech emotion classification task. The baseline approach models the information from audio and text independently using two deep neural networks (DNNs). The outputs from both the DNNs are then fused for…
In this paper, we are interested in exploring Deep Neural Network (DNN) based speaker embedding for Random-digit task using content information. To this end, a technique is applied to automatically select common phonetic units between the enrollment and test data to produce speaker verification scor…
Model-based approaches to Speaker Verification (SV), such as Joint Factor Analysis (JFA), i-vector and relevance Maximum-a-Posteriori (MAP), have shown to provide state-of-the-art performance for text-dependent systems with fixed phrases. The performance of i-vector and JFA models has been further e…
Multi-session training conditions are becoming increasingly common in recent benchmark datasets for both text-independent and text-dependent speaker verification. In the state-of-the-art i-vector framework for speaker verification, such conditions are addressed by simple techniques such as averaging…
The i-vector and Joint Factor Analysis (JFA) systems for text-dependent speaker verification use sufficient statistics computed from a speech utterance to estimate speaker models. These statistics average the acoustic information over the utterance thereby losing all the sequence information. In thi…
The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source domain data effectively. The i-vector based Probabilistic Linear Discriminant Analysis (PLDA) framework approaches this task by clustering the target domain data an…
This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significantly outperform traditional HMM/GMMs in Auto…