Personne

Jitendra Ajmera

Publications associées (20)

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An Online Audio Indexing System

Hervé Bourlard, Jitendra Ajmera

This paper presents overview of an online audio indexing system, which creates a searchable index of speech content embedded in digitized audio files. This system is based on our recently proposed offline audio segmentation techniques. As the data arrives ...

IDIAP2003

Robust Speaker Change Detection

Hervé Bourlard, Jitendra Ajmera

Most commonly used criteria for speaker change detection like log likelihood ratio (LLR) and Bayesian information criterion (BIC) have an adjustathreshold/penalty parameter to make speaker change decisions. These parameters robust to different acoustic con ...

2003

Clustering And Segmenting Speakers And Their Locations In Meetings

Guillaume Lathoud, Jitendra Ajmera

This paper presents a new approach toward automatic annotation of meetings in terms of speaker identities and their locations. This is achieved by segmenting the audio recordings using two independent sources of information : magnitude spectrum analysis an ...

IDIAP2003

Robust HMM-Based Speech/Music Segmentation

Hervé Bourlard, Jitendra Ajmera

In this paper we present a new approach towards high performance speech/music segmentation on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the local probability density function (PDF) estimators ...

2002

Improved Unknown-Multiple Speaker clustering using HMM

Hervé Bourlard, Jitendra Ajmera

In this report, we build up on our previous work on speaker clustering, where the number of speakers and segmentation boundaries are unknown a priori. We employ an ergodic HMM with minimum duration topology for this purpose. Starting from a large number of ...

IDIAP2002

Unknown-Multiple Speaker clustering using HMM

Hervé Bourlard, Jitendra Ajmera

An HMM-based speaker clustering framework is presented, where the number of speakers and segmentation boundaries are unknown \emph{a priori}. Ideally, the system aims to create one pure cluster for each speaker. The HMM is ergodic in nature with a minimum ...

2002

Unknown-Multiple Speaker clustering using HMM

Hervé Bourlard, Jitendra Ajmera

IDIAP2002

Robust Speaker Change Detection

Hervé Bourlard, Jitendra Ajmera

IDIAP2002

Robust HMM-Based Speech/Music Segmentation

Hervé Bourlard, Jitendra Ajmera

IDIAP2001

Speech/Music Discrimination using Entropy and Dynamism Features in a HMM Classification Framewor

Hervé Bourlard, Jitendra Ajmera

In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, the (local) Probability Density Function (PDF) estima ...

IDIAP2001