Bernt Andrassy

Comparison of Four Approaches to Age and Gender Recognition for Telephone Applications

This paper presents a comparative study of four different approaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with human performance on the same data. The automatic approaches compared are based on (1) a parallel phone recognizer, derived from an automatic language identification system; (2) a system using dynamic Bayesian networks to combine several prosodic features; (3) a system based solely on linear prediction analysis; and (4) Gaussian mixture models based on MFCCs for separate recognition of age and gender. On average, the parallel phone recognizer performs as well as Human listeners do, while loosing performance on short utterances. The system based on prosodic features however shows very little dependence on the length of the utterance.

ASR in mobile phones - an industrial approach

In order to make hidden Markov model (HMM) speech recognition suitable for mobile phone applications, Siemens developed a recognizer, Very Smart Recognizer (VSR), for deployment in future mobile phone generations. Typical applications will be name dialling, command and control operations suited for different environments, for example in cars. The paper describes research and development issues of a speech recognizer in mobile devices focusing on noise robustness, memory efficiency and integer implementation. The VSR is shown to reach a word error rate as low as 4.1% on continuous digits recorded in a car environment. Furthermore by means of discriminative training and HMM-parameter coding, the memory requirements of the VSR HMMs are smaller than 64 kBytes.

Investigations on the combination of four algorithms to increase the noise robustness of a DSR front-end for real world car data

This paper shows how the noise robustness of a MFCC feature extraction front-end can be improved by integrating four noise robustness algorithms:a spectral attenuation, a noise level normalisation, a cepstral mean normalization and a frame dropping algorithm. The algorithms were tested separately and in varying combinations on three real world car data sets with different amounts of mismatch between the training and the testing conditions. It was shown that although the algorithms partly have similar effects none of them is completely redundant. Every algorithm can contribute to a further improvement of the recognition results so the best results can be achieved by a combination of all four of them. A relative reduction of the word error rate of up to 57% is achieved.