Abstract: Noise and signal-to-noise ratio (SNR) estimation are relatively straightforward tasks. However, when SNR is small, systematic errors in measurement may result in over-estimation of SNR, which also occurs during runtime monitoring of SNR. Here, sufficient numbers of bits have been preassigned to each channel using QAM modulation scheme. Therefore, SNR relative to QAM lattice size depends on the noise margin and the desired (bit error rate) BER. If a relatively small margin is desired, similar measurement errors may result in over-estimation of SNR. Another problem that arises is that the variance of the noise estimator is relatively high. Therefore, SNR estimates may vary by several dB, and there is only 50% confidence in the usual estimators that the actual SNR value will not be worse than that estimated. Thus, a computationally efficient method for SNR estimation that also allows for specification of a confidence level in the estimates is provided.

Abstract: Methods and apparatus for enhancing the accuracy and efficacy of speech recognition in an environment, such as the interior of an automobile or home, in which there may be significant ambient noise, such as music from loudspeakers within an automobile or programming from a television within a home.

Abstract: Methods and apparatus for enhancing the accuracy and efficacy of speech recognition in an environment, such as the interior of an automobile or home, in which there may be significant ambient noise, such as music from loudspeakers within an automobile or programming from a television within a home.