Bio-inspired Dynamic Formant Tracking for Phonetic Labelling

It is a known fact that phonetic labeling may be relevant in helping current Automatic Speech Recognition (ASR) when combined with classical parsing systems as HMM's by reducing the search space. Through the present paper a method for Phonetic Broad-Class Labeling (PCL) based on speech perception in the high auditory centers is described. The methodology is based in the operation of CF (Characteristic Frequency) and FM (Frequency Modulation) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus in the automatic detection of formants and formant dynamics on speech. Results obtained informant detection and dynamic formant tracking are given and the applicability of the method to Speech Processing is discussed

Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.Comment: 200

Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks

The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.Comment: Accepted in Interspeech 201

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure