This paper compares two approaches of automatic age and gender classification with 7 classes. The first approach are Gaussian mixture models (GMMs) with universal background models (UBMs), which is well known for the task of speaker identification/verification. The training is performed by the EM algorithm or MAP adaptation respectively. For the second approach for each speaker of the test and training set a GMM model is trained. The means of each model are extracted and concatenated, which results in a GMM supervector for each speaker. These supervectors are then used in a support vector machine (SVM). Three different kernels were employed for the SVM approach: a polynomial kernel (with different polynomials), an RBF kernel and a linear GMM distance kernel, based on the KL divergence. With the SVM approach we improved the recognition rate to 74% (p < 0.001) and are in the same range as humans.

The problem of the effect of accent on the performance of Automatic Speech Recognition (ASR) systems is well known. In this paper, we study the effect of accent variability on the performance of the Indian English ASR task. We evaluate the test vocabularies on HMMs trained on (a) Accent specific training data (b) Accent pooled training data which combines all the accent specific training data (c) Accent pooled training data of reduced size matching the size of the accent specific training data. We demonstrate that the accent pooled training set performs the best on phonetically rich isolated word recognition task. But the accent specific HMMs perform better than the reduced accent pooled HMMs, indicating a possible approach of using a first stage accent identification to choose the correct accent trained HMMs for further recognition.

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.

The offline HMM adaptation of a generic car speech recognizer to a specific car environment is investigated. For the generation of the adaptation database the approach of Environment Adapted Databases (EADB) is applied that avoids real speech recordings in the target environment and therefore reduces the effort significantly. With MLLR adaptation using such an EADB a relative reduction of the word error rate of more than 10% can be achieved on a British city names task. It is proven by adaptation on real speech recordings from the target environment that the improvement with EADBs fully exploits the potential of HMM adaptation for the given car. Additionally it can be shown that if task matching material is available for adaptation a performance improvement of more than 30% can be reached with an additional maximum likelihood training iteration.

This paper presents the Siemens speech recognizer for mobile phones, VSR. VSR employs HMM technology and uses general-purpose phoneme-based acoustic models which make it speaker and vocabulary independent. The system can be easily reconfigured to work with arbitrary vocabularies. This provides full flexibility for the design of the user interface which contrasts with the capabilities of other low-resource recognizers. The system requirements of VSR are very low. The emission probability calculation and the Viterbi search with a vocabulary of 30 words need only 16 MHz for real-time operation on an ARM microcontroller. The HMM acoustic models take up about 12 kilobytes of permanent storage. The most significant algorithmic improvement is the newly developed 3-D stream-based coding of the HMMs. Despite low requirements in terms of system resources VSR achieves an outstanding recognition performance. The word error rate (WER) for a recognition task with 62 German isolated words including highly confusable digits is 7.0%.

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.

One of the most commonly used discriminative approaches in parameter estimation for Hidden Markov Models is the Minimum Classification Error (MCE) method ([1]). This paper studies possible choices for the classes (i.e. basic speech units) in MCE training and their application for several tasks suitable for speech driven dialog systems in the telephone environment. The considered choices of classes are HMM states, phonemes, words and sequences of words. The theoretical suitability and practical considerations for the different criteria are discussed. Using the different training criteria consistent experimental results are given for four tasks: non–task–specific training, training for small vocabulary isolated word recognition, training for connected digit recognition and for letter recognition. In all experiments not only the objective of the optimization but also the resulting word recognition performance is investigated. It shows that for the given setup only word and word string based criteria are capable to reduce the word error rate.

Entering city names is an important issue for various speech driven applications such as telephone directory assistance. This paper proposes a system that combines word recognition with utterance verification and spelling as a fall back strategy. Word recognition experiments show that the use of a medium size vocabulary yields the lowest error rate when only permitting very few false acceptances. For letter recognition discriminatively trained letter models are applied. In order to shorten the spelling procedure two abort conditions are introduced which reduce the number of letters that have to be spelled. The system handles 96.5% of all calls correctly while less than 45% of all callers must spell the name.

The paper studies the use of discriminative techniques for a telephone based isolated digit recognizer with respect to a reduced system complexity. The combination of linear discriminant analysis (LDA) and minimum error classification (MEC) training provides improved system performance at reduced costs for the training process and for the application. Experiments are performed on an isolated digit database recorded over public lines including approximately 700 speakers. The use of a single linear transformation matrix based on LDA allows the use of density modeling, that doesn't consider variances explicitly at a high recognition rate. Minimum classification error training is found to perform best in case of a small amount of system parameters. A reduction of error rate up to 80% was achieved by the combination of the two methods for such a system configuration.

A system for understanding time utterances spoken in German language is presented. Stochastic models contain the knowledge in the semantic, syntactic and acoustic-phonetic levels. An adequate semantic representation allows the integration of these models within a one-pass Viterbi search. The simultaneous use of all knowledge sources for the search procedure results in the smallest possible search space for the determination of the most probable semantic content accurately following the Bayes classification rule. Both the recognition accuracy and the computing speed facilitate a realistic application.