| Paper: | SLP-P3.11 | ||
| Session: | Novel LVCSR Algorithms | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Alternative Statistical and Machine Learning Methods for General ASR (e.g., no-HMM methods) | ||
| Title: | Using Pitch as Prior Knowledge in Template-Based Speech Recognition | ||
| Authors: | Guillermo Aradilla, Jithendra Vepa, Hervé Bourlard, IDIAP Research Institute, Switzerland | ||
| Abstract: | In a previous work on speech recognition, we showed that templates can better capture the dynamics of speech signal compared to parametric models such as hidden Markov models. The key point in template matching approaches is finding the most similar templates to the test utterance. Traditionally, this selection is given by a distortion measure on the acoustic features. In this work, we propose to improve this template selection with the use of meta-linguistic information as a prior knowledge. In this way, similarity is not only based on acoustic features but on other types of information that are also present in speech signal. Results on a continuous digit recognition task confirm the statement that similarity between words does not only depend on acoustic features since we obtained 24% relative improvement over the baseline. Interestingly, results are better even when compared to a system with no prior information but a larger number of templates. | ||
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