Philippe Gelin

Robust preprocessing signal equalization system and method for normalizing to a target environment

The audio source is spectrally shaped by filtering in the time domain to approximate or emulate a standardized or target microphone input channel. The background level is adjusted by adding noise to the time domain signal prior to the onset of speech to set a predetermined background noise level based on a predetermined target. The audio source is then monitored in real time and the signal-to-noise ratio is adjusted by adding noise to the time domain signal, in real time, to maintain a signal-to-noise ratio based on a predetermined target value. The normalized audio signal may be applied to both training speech and test speech. The resultant normalization minimizes the mismatch between training and testing and also improves other speech processing functions, such as speech endpoint detection.

N-best based supervised and unsupervised adaptation for native and non-native speakers in cars

A new set of techniques exploiting N-best hypotheses in supervised and unsupervised adaptation are presented. These techniques combine statistics extracted from the N-best hypotheses with a weight derived from a likelihood ratio confidence measure. In the case of supervised adaptation the knowledge of the correct string is used to perform N-best based corrective adaptation. Experiments run for continuous letter recognition recorded in a car environment show that weighting N-best sequences by a likelihood ratio confidence measure provides only marginal improvement as compared to 1-best unsupervised adaptation and N-best unsupervised adaptation with equal weighting. However, an N-best based supervised corrective adaptation method weighting correct letters positively and incorrect letters negatively, resulted in a 13% decrease of the error rate as compared with supervised adaptation. The largest improvement was obtained for non-native speakers.