This paper describes the ECESS evaluation campaign of voice activity and voicing detection. Standard VAD classifies signal into speech and non-speech, we extend it to VAD+ so that it classifies a signal as a sequence of non-speech, voiced and unvoiced segments. The evaluation is performed on a portion of the Spanish SPEECON database with manually labeled segmentation. To avoid errors caused by the limited precision of manual labeling we introduce "dead zones" -tolerance intervals ±5 ms around label changes in the data set. In these tolerance intervals we don't evaluate the signal.

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.