Phoneme and sentence-level ensembles for speech recognition

We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level and one at the utterance level, with a phoneme-level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal, while bagging significantly outperforms all other methods. We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition

A Subband-Based SVM Front-End for Robust ASR

This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels

Dichotic integration of acoustic-phonetic information: Competition from extraneous formants increases the effect of second-formant attenuation on intelligibility

Differences in ear of presentation and level do not prevent effective integration of concurrent speech cues such as formant frequencies. For example, presenting the higher formants of a consonant-vowel syllable in the opposite ear to the first formant protects them from upward spread of masking, allowing them to remain effective speech cues even after substantial attenuation. This study used three-formant (F1+F2+F3) analogues of natural sentences and extended the approach to include competitive conditions. Target formants were presented dichotically (F1+F3; F2), either alone or accompanied by an extraneous competitor for F2 (i.e., F1±F2C+F3; F2) that listeners must reject to optimize recognition. F2C was created by inverting the F2 frequency contour and using the F2 amplitude contour without attenuation. In experiment 1, F2C was always absent and intelligibility was unaffected until F2 attenuation exceeded 30 dB; F2 still provided useful information at 48-dB attenuation. In experiment 2, attenuating F2 by 24 dB caused considerable loss of intelligibility when F2C was present, but had no effect in its absence. Factors likely to contribute to this interaction include informational masking from F2C acting to swamp the acoustic-phonetic information carried by F2, and interaural inhibition from F2C acting to reduce the effective level of F2

Brain-Language Research: Where is the Progress?

Recent cognitive neuroscience research improved our understanding of where, when, how, and why language circuits emerge and activate in the human brain. Where: Regions crucial for very specific linguistic processes were delineated; phonetic features and fine semantic categories could be mapped onto specific sets of cortical areas. When: Brain correlates of phonological, syntactic and semantic processes were documented early-on, suggesting language understanding in an instant (within 250ms). How: New mechanistic network models mimicking structure and function of left-perisylvian language areas suggest that multimodal action-perception circuits — rather than separate modules for action and perception — carry the processing resources for language use and understanding. Why language circuits emerge in specific areas, become active at specific early time points and are connected in specific ways is best addressed in light of neuroscience principles governing neuronal activation, correlation learning, and, critical-ly, partly predetermined structural information wired into connections between cortical neurons and areas

Byte-based Language Identification with Deep Convolutional Networks

We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network's architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies.Comment: 7 pages. Adapted reviewer comments. arXiv admin note: text overlap with arXiv:1609.0705