Segmentation-based speech enhancement for intelligibility improvement in MELP coders using auxiliary sensors

Intelligibility of spoken words in noisy environments is an important problem of speech coders particularly for military applications. The intelligibility problem of a MELP speech encoder in noisy environments is addressed by using a novel speech enhancement algorithm at the front end. The speech signal is segmented into broad phonetic classes using auxiliary sensors in addition to the acoustic microphone. Each phoneme class is enhanced by suppressing maximum noise while minimally distorting perceptually important cues using the acoustic-phonetic knowledge about the class. The DRT (diagnostic rhyme tests) cores in an M2 tank noise environment show substantial improvement over the MELPe coder.