Personal Spoken Sentence Retrieval Using Two-Level Feature Matching and MPEG-7 Audio LLDs*
Po Chuan Lin, Jhing Fa Wang, Jia Ching Wang, Jun Huang · 2015
Conventional spoken sentence retrieval (SSR) relies on a large-vocabulary continuous-speech recognition (LVCSR) system. This investigation proposes a feature-based speaker-dependent SSR algorithm using two-level matching. Users can speak keywords as the query inputs to get the similarity ranks from a spoken sentence database. For instance, if a user is looking for a relevant personal spoken sentence, “October 12, I have a meeting in New York ” in the database, then the appropriate query input could be “meeting”, “New York ” or “October”. In the first level, a Similar Frame Tagging scheme is proposed to locate possible segments of the database sentences that are similar to the user’s query utterance. In the second level, a Fine Similarity Evaluation between the query and each possible segment is performed. Based on the feature-based comparison, the proposed al-gorithm does not require acoustic and language models, thus our SSR algorithm is language independent. Effective feature selection is the next issue in this paper. In addition to the conventional mel frequency cepstrum coefficients (MFCCs), several MPEG-7 audio low-level descriptors (LLDs) are also used as the features to exploit their ability for SSR. Ex-perimental results revealed that the retrieval performance using MPEG-7 audio LLDs