A multimicrophone approach to speech processing in a smart-room environment

Recent advances in computer technology and speech and language processing have made possible that some new ways of person-machine communication and computer assistance to human activities start to appear feasible. Concretely, the interest on the development of new challenging applications in indoor environments equipped with multiple multimodal sensors, also known as smart-rooms, has considerably grown. In general, it is well-known that the quality of speech signals captured by microphones that can be located several meters away from the speakers is severely distorted by acoustic noise and room reverberation. In the context of the development of hands-free speech applications in smart-room environments, the use of obtrusive sensors like close-talking microphones is usually not allowed, and consequently, speech technologies must operate on the basis of distant-talking recordings. In such conditions, speech technologies that usually perform reasonably well in free of noise and reverberation environments show a dramatically drop of performance. In this thesis, the use of a multi-microphone approach to solve the problems introduced by far-field microphones in speech applications deployed in smart-rooms is investigated. Concretely, microphone array processing is investigated as a possible way to take advantage of the multi-microphone availability in order to obtain enhanced speech signals. Microphone array beamforming permits targeting concrete desired spatial directions while others are rejected, by means of the appropriate combination of the signals impinging a microphone array. A new robust beamforming scheme that integrates an adaptive beamformer and a Wiener post-filter in a single stage is proposed for speech enhancement. Experimental results show that the proposed beamformer is an appropriate solution for high noise environments and that it is preferable to conventional post-filtering of the output of an adaptive beamformer. However, the beamformer introduces some distortion to the speech signal that can affect its usefulness for speech recognition applications, particularly in low noise conditions. Then, the use of microphone arrays for specific speech recognition purposes in smart-room environments is investigated. It is shown that conventional microphone array based speech recognition, consisting on two independent stages, does not provide a significant improvement with respect to single microphone approaches, especially if the recognizer is adapted to the actual acoustic environmental conditions. In the thesis, it is pointed out that speech recognition needs to incorporate information about microphone array beamformers, or otherwise, beamformers need to incorporate speech recognition information. Concretely, it is proposed to use microphone array beamformed data for acoustic model construction in order to take more benefit from microphone arrays. The result obtained with the proposed adaptation scheme with beamformed enrollment data shows a remarkable improvement in a speaker dependent recognition system, while only a limited enhancement is achieved in a speaker independent recognition system, partially due to the use of simulated microphone array data. On the other hand, a common limitation of microphone array processing is that a reliable speaker position estimation is needed to correctly steer the beamformer towards the position of interest. Additionally, knowledge about the location of the audio sources present in a room is information that can be exploited by other smart-room services, such as automatic video steering in conference applications. Fortunately, audio source tracking can be solved on the basis of multiple microphone captures by means of several different approaches. In the thesis, a robust speaker tracking system is developed based on successful state of the art SRP-PHAT algorithm, which computes the likelihood of each potential source position on the basis of the generalized cross-correlation estimations between pairs of microphones. The proposed system mainly incorporates two novelties: firstly, cross-correlations are adaptively computed based on the estimated velocities of the sources. The adaptive computation permits minimizing the influence of the varying dynamics of the speakers present in a room on the overall localization performance. Secondly, an accelerated method for the computation of the source position based on coarse-to-fine search strategies in both spatial and frequency dimensionalities is proposed. It is shown that the relation between spatial resolution and cross-correlation bandwidth is a matter of major importance in this kind of fast search strategies. Experimental assessment shows that the two novelties introduced permit achieving a reasonably good tracking performance in relatively controlled environments with few non-overlapping speakers. Additionally, the remarkable results obtained by the proposed audio tracker in an international evaluation confirm the convenience of the algorithm developed. Finally, in the context of the development of novel technologies that can provide additional cues of information to the potential services deployed in smart-room environments, acoustic head orientation estimation based on multiple microphones is also investigated in the thesis. Two completely different approaches are proposed and compared: on the one hand, sophisticated methods based on the joint estimation of speaker position and orientation are shown to provide a superior performance in exchange of large computational requirements. On the other hand, simple and computationally cheap approaches based on speech radiation considerations are suitable in some cases, such as when computational complexity is limited or when the source position is known beforehand. In both cases, the results obtained are encouraging for future research on the development of new algorithms addressed to the head orientation estimation problem.