This article presents a new feature extraction technique based on the temporal tracking of clusters in spectro-temporal features space. In the proposed method, auditory cortical outputs were clustered. The attributes of speech clusters were extracted as secondary features. However, the shape and position of speech clusters change during the time. The clusters temporally tracked and temporal tra...

A multi-stream approach to utilizing the inherently large number of spectro-temporal features for speech recognition is investigated in this study. Instead of reducing the featurespace dimension, this method divides the features into streams so that each represents a patch of information in the spectrotemporal response field. When used in combination with MFCCs for speech recognition under both...

Previous work has shown that spectro-temporal features reduce WER for automatic speech recognition under noisy conditions. The spectro-temporal framework, however, is not the only way to process features in order to reduce errors due to noise in the signal. The two-stage mel-warped Wiener filtering method used in the “Advanced Front End” (AFE), now a standard front end for robust recognition, i...

Unvoiced stops are rapidly varying sounds with acoustic cues to place identity linked to the temporal dynamics. Neurophysiological studies have indicated the importance of joint spectro-temporal processing in the human perception of stops. In this study, two distinct approaches to modeling the spectro-temporal envelope of unvoiced stop phone segments are investigated with a view to obtaining a ...

Introduction Despite the fact that the dynamic aspects of speech are very important, conventional speech features as Mel Ceptstral Coefficients (Mfccs) [1] and RelAtive SpecTrAl Perceptual Linear Predictive (Rasta-Plp) features [2] capture only stationary spectral information. We could previously show that a combination of conventional speech features with spectro-temporal speech features yield...

The auditory system extracts behaviorally relevant information from acoustic stimuli. The average activity in auditory cortex is known to be sensitive to spectro-temporal patterns in sounds. However, it is not known whether the auditory cortex also processes more abstract features of sounds, which may be more behaviorally relevant than spectro-temporal patterns. Using recordings from three stat...

We report progress in the use of multi-stream spectro-temporal features for both small and large vocabulary automatic speech recognition tasks. Features are divided into multiple streams for parallel processing and dynamic utilization in this approach. For small vocabulary speech recognition experiments, the incorporation of up to 28 dynamically-weighted spectro-temporal feature streams along w...

Gabor features have been proposed for extracting spectro-temporal modulation information, and yielding significant improvements in recognition performance. In this paper, we propose the integration of Gabor posteriors with MFCC posteriors, yielding a relative improvement of 14.3% over an MFCC Tandem system. We analyze for different types of acoustic units the complementarity between Gabor featu...

The effect of bio-inspired spectro-temporal processing for automatic speech recognition (ASR) is analyzed for two different tasks with focus on the robustness of spectro-temporal Gabor features in comparison to mel-frequency cepstral coefficients (MFCCs). Experiments aiming at extrinsic factors such as additive noise and changes of the transmission channel were carried out on a digit classifica...

We could show in the past that Hierarchical SpectroTemporal (HIST) features improve the performance of Automatic Recognition Systems (ARS) of speech in difficult environments when they are combined with conventional speech spectral features. The target here is to improve the noise robustness of the HIST features by investigating a channel distribution equalization in our feature hierarchy. Ther...