To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor...

Spectro-temporal filtering has been shown to result in features that can help to increase the robustness of automatic speech recognition (ASR) in the past. We replace the spectro-temporal representation used in previous work with spectrograms that incorporate knowledge about the signal processing of the human auditory system and which are derived from Power-Normalized Cepstral Coefficients (PNC...

In order to enhance automatic speech recognition performance in adverse conditions, localized spectro-temporal features (LSTF) are investigated, which are motivated by physiological measurements in the primary auditory cortex. In the Aurora2 experimental setup, Gabor-shaped LSTFs combined with a Tandem system yield robust performance with a feature set size of 30. If computational constraints a...

Although speech understanding is highly variable amongst cochlear implants (CIs) subjects, the remarkably high speech recognition performance of many CI users is unexpected and not well understood. Numerous factors, including neural health and degradation of the spectral information in the speech signal of CIs, likely contribute to speech understanding. We studied the ability to use spectro-tem...

We introduce the problem of phone classification in the context of speech recognition, and explore several sets of local spectro-temporal features that can be used for phone classification. In particular, we present some preliminary results for phone classification using two sets of features that are commonly used for object detection: Haar features and SVMclassified Histograms of Gradients (HoG).

How the temporal information that is crucial for understanding speech and music is processed in the brain is poorly understood, but a new study shows how the auditory cortex is tuned to the spectro-temporal acoustic features characteristic of natural biological sounds.

This paper analyzes the application of methods developed in automatic speech recognition (ASR) to better understand neural activity measured with electrocorticography (ECoG) during the presentation of speech. ECoG data is collected from temporal cortex in two subjects listening to a matrix sentence test. We investigate the relation of ECoG signals and acoustic speech that has been processed wit...

In this paper we consider the problem of modeling spectro-temporal behaviour of musical sounds, with applications for musical instrument recognition. Using instanteneous sound features, such as cepstral envelopes and cepstral derivatives, the temporal evolution of the sound is transcribed into a new representation as a sequence of spectral features. Applying information-theoretic sequence match...

In the present study we investigated the functional organization of sublexical auditory perception with specific respect to auditory spectro-temporal processing in speech and non-speech sounds. Participants discriminated verbal and nonverbal auditory stimuli according to either spectral or temporal acoustic features in the context of a sparse event-related functional magnetic resonance imaging ...

Recent results from physiological and psychoacoustic studies indicate that spectrally and temporally localized time-frequency envelope patterns form a relevant basis of auditory perception. This motivates new approaches to feature extraction for automatic speech recognition (ASR) which utilize two-dimensional spectro-temporal modulation filters. The paper provides a motivation and a brief overv...