Transforming HMMs for speaker-independent hands-free speech recognition in the car

In the absence of HMMs trained with speech collected in the target environment, one may use HMMs trained with a large amount of speech collected in another recording condition (e.g., quiet office, with high quality microphone). However, this may result in poor performance because of the mismatch between the two acoustic conditions. We propose a linear regression-based model adaptation procedure to reduce such a mismatch. With some adaptation utterances collected for the target environment, the procedure transforms the HMMs trained in a quiet condition to maximize the likelihood of observing the adaptation utterances. The transformation must be designed to maintain speaker-independence of the HMM. Our speaker-independent test results show that with this procedure about 1% digit error rate can be achieved for hands-free recognition, using target environment speech from only 20 speakers.