PHASE-BASED DUAL-MICROPHONE SPEECH ENHANCEMENT USING A PRIOR SPEECH MODEL Guangji Shi, M.A.Sc. Ph.D. Candidate University of Toronto Research Supervisor: Parham Aarabi This is a joint research with professor Hui Jiang from York University

Introduction Automatic speech recognition (ASR) can change our lives The performance of ASR systems degrade significantly in adverse environments Microphone array based speech enhancement techniques have gained popularity In the past, we proposed a phase-based dual-microphone filter

Phase-Based Dual-Microphone Filter

Parameter Estimation Using a Prior Speech Model Although the phase-based filter is effective in suppressing noise, its effectiveness is determined by its parameter value We propose to estimate the optimal parameter adaptively using a prior speech model

Parameter Estimation Using a Prior Speech Model Train the prior speech model with training data For each test utterance, estimate the optimal parameter iteratively Process the test utterance using the optimized phase-error filter Decode the filtered utterance using an HMM based speech recognizer

Performance Evaluation The performance of the proposed algorithm is evaluated using the CARVUI database from Bell Labs 50 speakers for training 3 speakers for testing (524 utterances) (524 utterances) babble noise SNR = 0dB

Questions?