Single- and Dual-Microphone-Based Pre-Processing Algorithms for Robust Speech Communication

Abstract

In general, the quality of speech signal is often contaminated by various types of background noise, which causing significant degradation of performance in speech processing systems. Pre-processing algorithms such as speech enhancement and voice activity detection (VAD) in enhancing the corrupted speech are considered. An important task of pre-processing algorithms for speech processing is to estimate the desired signals and reduce the background noise. In this regard, this dissertation presents single- and dual-microphone-based pre-processing algorithms for robust speech communication. In the first part of the dissertation, we present a statistical model-based single-microphone speech enhancement technique using acoustic environment classification supported by a Gaussian mixture model (GMM). In the data training stage, the principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method, the long-term smoothing parameter of the noise estimation, and the control parameter of the minimum gain value are uniquely set as optimal operating points according to the given noise information to ensure the best performance for each noise. These optimal operating points, which are specific to the different background noises, are estimated based on the composite measures, which are the objective quality measures representing the highest correlation with the actual speech quality processed by noise suppression algorithms. In the on-line environment-aware speech enhancement step, the noise classification is performed on a frame-by-frame basis using the maximum likelihood (ML)-based Gaussian mixture model (GMM). The speech absence probability (SAP) is used to detect the speech absence periods and to update the likelihood of the GMM. According to the classified noise information for each frame, we assign the optimal values to the aforementioned three parameters for speech enhancement. We evaluated the performances of the proposed methods using objective speech quality measures and subjective listening tests under various noise environments. Our experimental results showed that the proposed method yields better performances than does a conventional algorithm with fixed parameters. In the next part of the dissertation, we propose a novel dual-microphone voice activity detection technique based on the two-step power level difference (PLD) ratio. This technique basically exploits the PLD between the primary microphone and the secondary microphone in a mobile device when the distance between the microphones and the sound source is relatively short. Based on the PLD, we propose the use of the PLD ratio (PLDR) instead of the original PLD to take advantage of the relative difference between the PLD of speech and the PLD of noise. Indeed, the PLDR is obtained by estimating the ratio of the PLD between the input signals and the PLD between the two channel noises during periods without speech. The proposed technique offers a two-step algorithm using the PLDRs including a long-term PLDR (LT-PLDR), which characterizes long-term evolution and short-term PLDR (ST-PLDR), which characterizes short-time variation during the first step. LT-PLDR-based and ST-PLDR-based VAD decisions are performed using the maximum a posteriori (MAP) probability derived from the model-trust algorithm and combined at the second step to reach a superior VAD decision for both long-term and short-term situations. Extensive experimental results show that the proposed dual-microphone VAD technique outperforms the conventional two-channel VAD method as well as most standardized VAD algorithms.