One of the most efficient mechanisms to optimize signal-to-noise ratios is the Lombard effect - an involuntary rise in call amplitude due to ambient noise. It is often accompanied by changes in the spectro-temporal composition of calls. We examined the effects of broadband-filtered noise on the spectro-temporal composition of horseshoe bat echolocation calls, which consist of a constant-frequen...

Speech emotion recognition is mostly considered in clean speech. In this paper, joint spectro-temporal features (RS features) are extracted from an auditory model and are applied to detect the emotion status of noisy speech. The noisy speech is derived from the Berlin Emotional Speech database with added white and babble noises under various SNR levels. The clean train/noisy test scenario is in...

We present a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to (i) enable invariance of receptive field responses under natural sound transformations and (ii) ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequ...

We introduce a novel spectro-temporal representation of speech by applying directional derivative filters to the Melspectrogram, with the aim of improving the robustness of automatic speech recognition. Previous studies have shown that two-dimensional wavelet functions, when tuned to appropriate spectral scales and temporal rates, are able to accurately capture the acoustic modulations of speec...

In this study, the effect of speech-intrinsic variabilities such as speaking rate, effort and speaking style on automatic speech recognition (ASR) is investigated. We analyze the influence of such variabilities as well as extrinsic factors (i.e., additive noise) on the most common features in ASR (mel-frequency cepstral coefficients and perceptual linear prediction features) and spectro-tempora...

Mel-frequency cepstral coefficients (MFCCs) are the most popular features for automatic audio classification (AAC). However, MFCCs are often not robust in adverse environment. In this paper, a minimum classification error (MCE)-based method is proposed to extract new and robust spectro-temporal features as alternatives to MFCCs. The robustness of the proposed new features is evaluated on noisy ...

A novel type of feature extraction for automatic speech recognition is investigated. Two-dimensional Gabor functions, with varying extents and tuned to different rates and directions of spectro-temporal modulation, are applied as filters to a spectro-temporal representation provided by mel spectra. The use of these functions is motivated by findings in neurophysiology and psychoacoustics. Data-...

A novel type of feature extraction is introduced to be used as a front end for automatic speech recognition (ASR). Two-dimensional Gabor filter functions are applied to a spectro-temporal representation formed by columns of primary feature vectors. The filter shape is motivated by recent findings in neurophysiology and psychoacoustics which revealed sensitivity towards complex spectro-temporal ...

The robustness of the human auditory system to noise is partly due to the peak preserving capability of the periphery and the cortical filtering of spectro-temporal modulations. In this letter, a robust speech feature extraction scheme is developed that emulates this processing by deriving a spectrographic representation that emphasizes the high energy regions. This is followed by a modulation ...

A novel type of feature extraction for automatic speech recognition is investigated. Two-dimensional Gabor functions, with varying extents and tuned to different rates and directions of spectro-temporal modulation, are applied as filters to a spectro-temporal representation provided by mel spectra. The use of these functions is motivated by findings in neurophysiology and psychoacoustics. Data-...