Stochastic techniques in deriving perceptual knowledge

The paper argues on examples of selected past works that stochastic and knowledge-based approaches do not contradict each other. Frequency resolution of human hearing is decreasing with increasing frequency. Spectral basis designed for optimal discrimination among different phonemes of speech have similar property. Further, human hearing is most sensitive to modulations with frequency around 4 Hz. Filters on feature trajectories, designed for optimal discrimination among phonemes of speech are bandpass with central frequency around 4 Hz

Las fortalezas castellanas de la Orden de Calatrava en el siglo XII

In this paper, we present a spectro-temporal feature extraction technique using sub-band Hilbert envelopes of relatively long segments of speech signal. Hilbert envelopes of the sub-bands are estimated using Frequency Domain Linear Prediction (FDLP). Spectral features are derived by integrating the sub-band Hilbert envelopes in short-term frames and the temporal features are formed by converting the FDLP envelopes into modulation frequency components. These are then combined at the phoneme posterior level and are used as the input features for a phoneme recognition system. In order to improve the robustness of the proposed features to telephone speech, the sub-band temporal envelopes are gain normalized prior to feature extraction. Phoneme recognition experiments on telephone speech in the HTIMIT database show significant performance improvements for the proposed features when compared to other robust feature techniques (average relative reduction of $11\%$ in phoneme error rate)

On the Combination of Auditory and Modulation Frequency Channels for ASR applications

This paper investigates the combination of evidence coming from different frequency channels obtained filtering the speech signal at different auditory and modulation frequencies. In our previous work \cite{icassp2008}, we showed that combination of classifiers trained on different ranges of {\it modulation} frequencies is more effective if performed in sequential (hierarchical) fashion. In this work we verify that combination of classifiers trained on different ranges of {\it auditory} frequencies is more effective if performed in parallel fashion. Furthermore we propose an architecture based on neural networks for combining evidence coming from different auditory-modulation frequency sub-bands that takes advantages of previous findings. This reduces the final WER by 6.2\% (from 45.8\% to 39.6\%) w.r.t the single classifier approach in a LVCSR task

Hierarchical and Parallel Processing of Modulation Spectrum for ASR applications

The modulation spectrum is an efficient representation for describing dynamic information in signals. In this work we investigate how to exploit different elements of the modulation spectrum for extraction of information in automatic recognition of speech (ASR). Parallel and hierarchical (sequential) approaches are investigated. Parallel processing combines outputs of independent classifiers applied to different modulation frequency channels. Hierarchical processing uses different modulation frequency channels sequentially. Experiments are run on a LVCSR task for meetings transcription and results are reported on the RT05 evaluation data. Processing modulation frequencies channels with different classifiers provides a consistent reduction in WER (2\% absolute w.r.t. PLP baseline). Hierarchical processing outperforms parallel processing. The largest WER reduction is obtained trough sequential processing moving from high to low modulation frequencies. This model is consistent with several perceptual and physiological studies on auditory processing

Towards ASR Based on Hierarchical Posterior-Based Keyword Recognition

The paper presents an alternative approach to automatic recognition of speech in which each targeted word is classified by a separate binary classifier against all other sounds. No time alignment is done. To build a recognizer for N words, N parallel binary classifiers are applied. The system first estimates uniformly sampled posterior probabilities of phoneme classes, followed by a second step in which a rather long sliding time window is applied to the phoneme posterior estimates and its content is classified by an artificial neural network to yield posterior probability of the keyword. On small vocabulary ASR task, the system still does not reach the performance of the state-of-the-art system but its conceptual simplicity, the ease of adding new target words, and its inherent resistance to out-of-vocabulary sounds may prove significant advantage in many applications

Discriminant linear processing of time-frequency plane

Extending previous works done on considerably smaller data sets, the paper studies linear discriminant analysis of about 30 hours of phoneme-labeled speech data in the time-frequency domain. Analysis is carried both independently in time and frequency and jointly. Data driven spectral basis show similar frequency sensitivity as human hearing. LDA-derived temporal FIR filters are consistent with temporal lateral inhibition. Considerable improvement is obtained using first temporal discriminant

Identifying unexpected words using in-context and out-of-context phoneme posteriors

The paper proposes and discusses a machine approach for identification of unexpected (zero or low probability) words. The approach is based on use of two parallel recognition channels, one channel employing sensory information from the speech signal together with a prior context information provided by the pronunciation dictionary and grammatical constraints, to estimate `in-context' posterior probabilities of phonemes, the other channel being independent of the context information and entirely driven by the sensory data to deliver estimates of `out-of-context' posterior probabilities of phonemes. A significant mismatch between the information from these two channels indicates unexpected word. The viability of this concept is demonstrated on identification of out-of-vocabulary digits in continuous digit streams. The comparison of these two channels provides a confidence measure on the output of the recognizer. Unlike conventional confidence measures, this measure is not relying on phone and word segmentation (boundary detection), thus it is not affected by possibly imperfect segment boundary detection. In addition, being a relative measure, it is more discriminative than the conventional posterior based measures