IEEE International Conference on Acoustics Speech and Signal Processing · 2002 · 43 citations · 17 references
ComputingSpeech-language PathologyAcousticsNatural Language ProcessingPhoneticsAcoustic PhoneticsLanguage ModelingPattern RecognitionSpeech AcousticsSpeech RecognitionSpeech Recognition TestingAcoustic Feature ExtractionAutomatic SegmentationRobust Speech RecognitionSpeech Pattern RecognitionSpeech Recognition AcousticsSpeech Recognition EvaluationSpeech SegmentationVariable Acoustics
We report on the system IBM fielded in the second SPeech In Noisy Environments (SPINE-2) evaluation, conducted by the Naval Research Laboratory in October 2001. The key components of the system include an HMM-based automatic segmentation module using a novel set of LDA-transformed voicing and energy features, a multiple-pass decoding strategy that uses several speaker-and environment-normalization operations to deal with the highly variable acoustics of the evaluation, the combination of hypotheses from decoders operating on three distinct acoustic feature sets, and a class-based language model that uses both the SPINE-1 and SPINE-2 training data to estimate reliable probabilities for the new SPINE-2 vocabulary.
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