Oral, head and neck cancer represents 3% of all cancers in the United States and is the 6th most common cancer worldwide. Depending on the tumor size, location and staging, patients are treated by radical surgery, radiology, chemotherapy or a combination of those treatments. As a result, their anatomical structures for speech are impaired and this leads to some negative impact on their speech i...

Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this prob...

In this paper we present a method of sound classification which exploits a parts-based representation of spectrotemporal sounds, employing the nonnegative matrix factorization (NMF) [1]. We illustrate a new way of learning nonnegative features using a variant of NMF and show its useful behavior in the task of general sound classification with comparison to independent component analysis (ICA) w...

Speech recognition robust against interfering noise remains a difficult task. We previously presented a set of spectrotemporal speech features which we termed Hierarchical Spectro-Temporal (HIST) features showing improved robustness, especially when combined with RASTA-PLP. They are inspired by the receptive fields found in the mammalian auditory cortex and are organized in two hierarchical lev...

To overcome limitations of purely spectral speech features we previously introduced Hierarchical Spectro-Temporal (HIST) features. We could show that a combination of HIST and standard features does reduce recognition errors in clean and in noise. The HIST features consist of two hierarchical layers where the corresponding filter functions are learned in a data driven way. In this paper we inve...

An auditory-perception inspired singing voice separation algorithm for monaural music recordings is proposed in this paper. Under the framework of computational auditory scene analysis (CASA), the music recordings are first transformed into auditory spectrograms. After extracting the spectral-temporal modulation contents of the timefrequency (T-F) units through a two-stage auditory model, we de...

We present a novel architecture for word-spotting which is trained from a small number of examples to classify an utterance as containing a target keyword or not. The word-spotting architecture relies on a novel feature set consisting of a set of ordered spectro-temporal patches which are extracted from the exemplar mel-spectra of target keywords. A local pooling operation across frequency and ...

Spectro-temporal Gabor features based on auditory knowledge have improved word accuracy for automatic speech recognition in the presence of noise. In previous work, we generated robust spectro-temporal features that incorporated the power normalized cepstral coefficient (PNCC) algorithm. The corresponding power normalized spectrum (PNS) is then processed by many Gabor filters, yielding a high d...

In the last decade, several studies have shown that the robustness of ASR systems can be increased when 2D Gabor filters are used to extract specific modulation frequencies from the input pattern. This paper analyzes important design parameters for spectro-temporal features based on a Gabor filter bank: We perform experiments with filters that exhibit different phase sensitivity. Further, we an...