Abstract
This paper presents results of speaker-independent speech recognition experiments concerning acoustic front-ends, models and their structures in car environments. The database comprises 350 speakers in 6 different cars. We investigate whole-word models, context-independent phoneme models and context-dependent within-word phoneme models. We studied task-dependent (same vocabulary context in training and test) phoneme models and present first results on task-independent (broad context in training, i.e. phonetically rich material) scenarios. The latter allows flexible vocabulary definition for applications with dynamically changing command words or new applications avoiding an expensive data collection. Acoustic preprocessing is carried out with mel-cepstrum combined with spectral subtraction and SNR normalization. The task-dependent word error rates are well below 3% for both whole-word and phoneme models. The task-independent scenarios have to be worked on further.
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