Spatio-Temporal Analysis of Spontaneous Speech with Microphone Arrays
| Type of publication: | Thesis |
| Citation: | lathoud-thesis |
| Year: | 2006 |
| Month: | 12 |
| School: | Ecole Polytechnique Fédérale de Lausanne |
| Address: | Lausanne, Switzerland |
| Note: | PhD Thesis \#3689 at the Ecole Polytechnique Fédérale de Lausanne |
| Crossref: | lathoud-rr-06-77: |
| Abstract: | Accurate detection, localization and tracking of multiple moving speakers permits a wide spectrum of applications. Techniques are required that are versatile, robust to environmental variations, and not constraining for non-technical end-users. Based on distant recording of spontaneous multiparty conversations, this thesis focuses on the use of microphone arrays to address the question Who spoke where and when?. The speed, the versatility and the robustness of the proposed techniques are tested on a variety of real indoor recordings, including multiple moving speakers as well as seated speakers in meetings. Optimized implementations are provided in most cases. We propose to discretize the physical space into a few sectors, and for each time frame, to determine which sectors contain active acoustic sources (Where? When?). A topological interpretation of beamforming is proposed, which permits both to evaluate the average acoustic energy in a sector for a negligible cost, and to locate precisely a speaker within an active sector. One additional contribution that goes beyond the eld of microphone arrays is a generic, automatic threshold selection method, which does not require any training data. On the speaker detection task, the new approach is dramatically superior to the more classical approach where a threshold is set on training data. We use the new approach into an integrated system for multispeaker detection-localization. Another generic contribution is a principled, threshold-free, framework for short-term clustering of multispeaker location estimates, which also permits to detect where and when multiple trajectories intersect. On multi-party meeting recordings, using distant microphones only, short-term clustering yields a speaker segmentation performance similar to that of close-talking microphones. The resulting short speech segments are then grouped into speaker clusters (Who?,',','), through an extension of the Bayesian Information Criterion to merge multiple modalities. On meeting recordings, the speaker clustering performance is signicantly improved by merging the classical mel-cepstrum information with the short-term speaker location information. Finally, a close analysis of the speaker clustering results suggests that future research should investigate the effect of human acoustic radiation characteristics on the overall transmission channel, when a speaker is a few meters away from a microphone. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
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| Added by: | [UNK] |
| Total mark: | 0 |
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