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Article publicat a Arxiv, sense revisió per parells In the analysis of acoustic scenes, often the occurring sounds have to be detected in time, recognized, and localized in space. Usually, each of these tasks is done separately. In this paper, a model-based approach to jointly carry them out for the case of multiple simultaneous sources is presented and tested. The recognized event classes and their respective room positions are obtained with a single system that maximizes the combination of a large set of scores, each one resulting from a different acoustic event model and a different beamformer output signal, which comes from one of several arbitrarily-located small microphone arrays. By using a two-step method, the experimental work for a specific scenario consisting of meeting-room acoustic events, either isolated or overlapped with speech, is reported. Tests carried out with two datasets show the advantage of the proposed approach with respect to some usual techniques, and that the inclusion of estimated priors brings a further performance improvement.

If speech is captured by several arbitrarily-located microphones in a room, the degree of distortion by noise and reverberation may vary strongly from one channel to another. Channel selection for automatic speech recognition aims to rank the signals according to their quality, and, in particular, to select the best one for further processing in the recognition system. To create this ranking, we propose here to use posterior probabilities estimated from the N-best hypothesis of each channel. When evaluated experimentally, this new channel selection technique outperforms the methods published so far. We also propose the combination of different channel selection techniques to further increase the recognition accuracy and to reduce the computational load without significant performance loss.

The performance of ASR systems in a room environment with distant microphones is strongly affected by reverberation. As the degree of signal distortion varies among acoustic channels (i.e. microphones), the recognition accuracy can benefit from a proper channel selection. In this paper, we experimentally show that there exists a large margin for WER reduction by channel selection, and discuss several possible methods which do not require any a-priori classification. Moreover, by using a LVCSR task, a significant WER reduction is shown with a simple technique which uses a measure computed from the sub-band time envelope of the various microphone signals.

El projecte es desenvolupa en el marc de la titulació oficial de màster MERIT del Departament de Teoria del Senyal i Comunicacions. El màster, orientat a la recerca i integrat dins del programa Erasmus Mundus, presenta trets esfecífics donat l’origen variat dels estudiants i que s’imparteix integrament en anglès. El projecte s’articula en 4 eixos: 1. Millorar el sistema d’acollida i integració dels estudiants en el programa formatiu del màster mitjançant la creació de recursos docents en xarxa (campus digital), i la incorporació de nous elements tecnològics a l’aula. 2. Millorar el seguiment dels estudiants de primer curs mitjançant suport als professors per part d’estudiants avantatjats de segon curs. 3. Fomentar el desenvolupament de competències transversals dels estudiants mitjançant la impartició d’assignatures que fomentin l’esperit crític i emprenedor. 4. Establir un mecanisme d’assegurament de la qualitat basat en un Comitè d’Avaluació Extern (CAE) d’acord amb les directrius de l’AQU

When applied to interactive seminars, the detection of acoustic events from only audio information shows a large amount of errors, which are mostly due to the temporal overlaps of sounds. Video signals may be a useful additional source of information to cope with that problem for particular events. In this work, we aim at improving the detection of steps by using two audio-based Acoustic Event Detection (AED) systems, with SVM and HMM, and a video-based AED system, which employs the output of a 3D video tracking algorithm. The fuzzy integral is used to fuse the outputs of the three detection systems. Experimental results using the CLEAR 2007 evaluation data show that video information can be successfully used to improve the results of audio-based AED.

A correct choice of voice signal modeling methods is essential to obtain good results in automatic speech recognition. In this paper, we have proposed a unified view of the speech parametrization stage, in which conventional techniques as Linear Prediction Coeficientes and mel-cepstrum filter bank are viewed as particular cases. The model incorporates a new technique of deconvolution, that is called root homomorphic deconvolution. A broad set of experimental results are also presented

The time sequences of speech parameters resulting from current short-time spectral estimators show a tradeoff between estimation error variance and time and frequency resolution. In this paper, we apply frequency analysis and linear filtering to these sequences to gain insights into their limitations and to provide an interpretation framework for several parameter processing techniques proposed in the past. Particularly, the observation of their long-term spectrum reveals the importance of band equalization for improving discrimination in speech recognition. Based on that, we propose a method of filtering the sequences that includes an explicit equalization and incorporates a bandwidth parameter. By using Slepian sequences in the design of the filters, good results were obtained in our preliminary word recognition tests.

The performance of existing speech recognition systems degrades rapidly in the presence of background noise when training and testing cannot be done under the same ambient conditions. The aim of this paper is to report the application of several robust techniques on a system based on the HMM (Hidden Markov Models) and VQ (Vector Quatization) approaches for speech recognition in car environment optimization of spectral model order and cepstral lifter, parameterizatrion based on the AR modeling of the autocorrelation sequence -proposed by the authors in [1]-, use of dynamic information and multilabeling.

Concepts and techniques involved in speech communication with computers are reviewed. Although the speech output is, at present, closer to the computer ability than the speech understanding, at the moment only partial results for both purposes have been attained.