Using Broad Phonetic Classes to Guide Search in Automatic Speech Recognition

Stefan Ziegler (1) , Bogdan Ludusan (2) , Guillaume Gravier (2)

1 METISS - Speech and sound data modeling and processing
2 TEXMEX - Multimedia content-based indexing

This work presents a novel framework to guide the Viterbi decoding process of a hidden Markov model based speech recognition system by means of broad phonetic classes. In a first step, decision trees are employed, along with frame and segment based attributes, in order to detect broad phonetic classes in the speech signal. Then, the detected phonetic classes are used to reinforce paths in the search process, either at every frame or at phonetically significant landmarks. Results obtained on French broadcast news data show a relative improvement in word error rate of about 2% with respect to the baseline.

Domains: Multimedia [cs.MM]

Stefan Ziegler, Bogdan Ludusan, Guillaume Gravier. Using Broad Phonetic Classes to Guide Search in Automatic Speech Recognition. INTERSPEECH - Annual Conference of the International Speech Communication Association, 2012, United States. ⟨hal-00758427⟩