Doctor of Philosophy, The Ohio State University, 2012, Computer Science and Engineering

In real-world listening environments, speech reaching our ear is often accompanied by acoustic interference such as environmental sounds, music or another voice. Noise distorts speech and poses a substantial difficulty to many applications including hearing aid design and automatic speech recognition. Monaural speech segregation refers to the problem of separating speech based on only one recording and is a widely regarded challenge. In the last decades, significant progress has been made on this problem but the challenge remains. This dissertation addresses monaural speech segregation from different interference. First, we research the problem of unvoiced speech segregation which is less studied compared to voiced speech segregation probably due to its difficulty. We propose to utilize segregated voiced speech to assist unvoiced speech segregation. Specifically, we remove all periodic signals including voiced speech from the noisy input and then estimate noise energy in unvoiced intervals using noise-dominant time-frequency units in neighboring voiced intervals. The estimated interference is used by a subtraction stage to extract unvoiced segments, which are then grouped by either simple thresholding or classification. We demonstrate that the proposed system performs substantially better than speech enhancement methods. Interference can be nonspeech signals or other voices. Cochannel speech refers to a mixture of two speech signals. Cochannel speech separation is often addressed by model-based methods, which assume speaker identities and pretrained speaker models. To address this speaker-dependency limitation, we propose an unsupervised approach to cochannel speech separation. We employ a tandem algorithm to perform simultaneous grouping of speech and develop an unsupervised clustering method to group simultaneous streams across time. The proposed objective function for clustering measures the speaker difference of each hypothesized grouping and incorporates pitch (open full item for complete abstract)

Committee: DeLiang Wang (Committee Chair); Eric Fosler-Lussier (Committee Member); Mikhail Belkin (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2006, Biophysics

In a natural environment, speech often occurs simultaneously with acoustic interference. Many applications, such as automatic speech recognition and telecommunication, require an effective system that segregates speech from interference in the monaural (one-microphone) situation. While this task of monaural speech segregation has proven to be very challenging, human listeners show a remarkable ability to segregate an acoustic mixture and attend to a target sound, even with one ear. This perceptual process is called auditory scene analysis (ASA). Research in ASA has inspired considerable effort in constructing computational ASA (CASA) based on ASA principles. Current CASA systems, however, face a number of challenges in monaural speech segregation. This dissertation presents a systematic and extensive effort in developing a CASA system for monaural speech segregation that addresses several major challenges. The proposed system consists of four stages: Peripheral analysis, feature extraction, segmentation, and grouping. In the first stage, the system decomposes the auditory scene into a time-frequency representation via bandpass filtering and time windowing. The second stage extracts auditory features corresponding to ASA cues, such as periodicity, amplitude modulation, onset and offset. In the third stage, the system segments an auditory scene based on a multiscale analysis of onset and offset. The last stage includes an iterative algorithm that simultaneously estimates the pitch of a target utterance and segregates the voiced target based on a pitch estimate. Finally, our system sequentially groups voiced and unvoiced portions of the target speech for non-speech interference, and this grouping task is performed using feature-based classification. Systematic evaluation shows that the proposed system extracts a majority of target speech without including much interference. Extensive comparisons demonstrate that the system has substantially advanced the state-of-the-ar (open full item for complete abstract)

Committee: DeLiang Wang (Advisor); William Masters (Other); Eric Fosler-Lussier (Other) Subjects: