Unsupervised Motif Acquisition in Speech via Seeded Discovery and Template Matching Combination

This paper describes and evaluates a computational architecture to discover and collect occurrences of speech repetitions, or motifs, in a totally unsupervised fashion, that is in the absence of acoustic, lexical or pronunciation modeling and training material. In the last few years, this task has known an increasing interest from the speech community because of a) its potential applicability in spoken document processing (as a preliminary step to summarization, topic clustering, etc.) and b) its novel methodology, that defines a new paradigm to speech processing that circumvents the issues common to all supervised, trained technologies. The contributions implied by the proposed system are two-fold: 1) the design of a discovery strategy that detects repetitions by extending matches of motif fragments, called seeds; 2) the implementation of template matching techniques to detect acoustically close segments, based on dynamic time warping (DTW) and self-similarity matrix (SSM) comparison of speech templates, in contrast to the decoding procedures of model-based recognition systems. The architecture is thoroughly evaluated on several hours of French broadcast news shows according to various parameter settings and acoustic features, namely mel-frequency cepstral coefficients (MFCCs) and different types of posteriorgrams: Gaussian mixture model (GMM)-based, and phone-based posteriors, in both language-matched and mismatched conditions. The evaluation highlights a) the improved robustness of the system that jointly employs DTW and SSM and b) the relevant impact of language-specific features to acoustic similarity detection based on template matching.