Christophe Lévy

Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data

This paper presents a novel fully automatic bi-modal, face and speaker, recognition system which runs in real-time on a mobile phone. The implemented system runs in real-time on a Nokia N900 and demonstrates the feasibility of performing both automatic face and speaker recognition on a mobile phone. We evaluate this recognition system on a novel publicly-available mobile phone database and provide a well defined evaluation protocol. This database was captured almost exclusively using mobile phones and aims to improve research into deploying biometric techniques to mobile devices. We show, on this mobile phone database, that face and speaker recognition can be performed in a mobile environment and using score fusion can improve the performance by more than 25% in terms of error rates.

Mobile Biometrics: Combined Face and Voice Verification for a Mobile Platform

The Mobile Biometrics (MoBio) project combines real-time face and voice verification for better security of personal data stored on, or accessible from, a mobile platform.

Reducing computational and memory cost for cellular phone embedded speech recognition system

We present several methods able to fit speech recognition system requirements to cellular phone resources. The proposed techniques are evaluated on a digit recognition task using both French and English corpora. We investigate particularly three aspects of speech processing: acoustic parameterization, recognition algorithms; acoustic modeling. Several parameterization algorithms (LPCC, MFCC and PLP) are compared to the linear predictive coding (LPC) included in the GSM norm. The MFCC and PLP parameterization algorithms perform significantly better than the others. Moreover, feature vector size can be reduced to 6 PLP coefficients, allowing memory and computation resources to be decreased without a significant loss of performance. In order to achieve good performance with reasonable resource needs, we develop several methods to embed a classical HMM-based speech recognition system in a cellular phone. We first propose an automatic on-line building of a phonetic lexicon which allows a minimal but unlimited lexicon. Then we reduce the HMM complexity by decreasing the number of (Gaussian) components per state. Finally, we evaluate our propositions by comparing dynamic time warping (DTW) with our HMM system - in the cellular phone context - for clean conditions. The experiments show that our HMM system outperforms DTW for speaker independent tasks and allows more practical applications for the cellular-phone user interface.

Mistral: open source biometric platform

Mistral is an open source software for biometrics applications. This software, based on the well-known UBM/GMM approach includes also the latest speaker recognition developments such as latent factor analysis, unsupervised adaptation or SVM supervectors. The software performance is highlighted in the framework of the NIST evaluation campaigns.

Compact acoustic models for embedded speech recognition

Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques) with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.

Embedded Mobile Phone Digit-Recognition

Speech recognition applications are known to require substantial amount of resources in terms of training data, memory and computing power. However, the targeted context of this work — embedded mobile phone speech recognition systems — only authorizes few KB of memory, few MIPS and usually a small amount of training data. In order to meet the resource constraints, an approach based on an HMM system using a GMM-based state-independent acoustic modeling is proposed in this paper. A transformation is computed and applied to the global GMM in order to obtain each of the HMM state-dependent probability density functions. This strategy aims at storing only the transformation function parameters for each state and enables to decrease the amount of computing power needed for the likelihood computation. The proposed approach is evaluated with a digit recognition task using the French corpus BDSON. Our method allows a Digit Error Rate (DER) of 2.1%, when the system respects the resource constraints. Compared to a standard HMM with comparable resources, our approach achieved a relative DER decrease of about 52%.

Fast discriminant training of semi-continuous HMM

In this paper, we introduce a fast estimate algorithm for discriminant training of semi-continuous HMM (Hidden Markov Models). We first present the Frame Discrimination (FD) method proposed in [1] for weight re-estimate. Then, the weight update equation is formulated in the specific framework of semi-continuous models. Finally, we propose an approximated update function which requires a very low level of computational resources. The first experiments validate this method by comparing our fast discriminant weighting (FDW) to the original one. We observe that, on a digit recognition task, FDW and FD estimate obtain similar results, when our method decreases significantly the computational time. A second experiment evaluates FDW in Large Vocabulary Continuous Speech Recognition (LVCSR) task. We incorporate semi-continuous FDW models in a Broadcast News (BN) transcription system. Experiments are carried out in the framework of ESTER evaluation campaign ([2]). Results show that in particular context of very compact acoustic models, discriminant weights improve the system performance compared to both a baseline continuous system and a SCHMM trained by MLE algorithm.