Phoneme alignment based on discriminative learning

Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan

Research output: Contribution to conferencePaperpeer-review

18 Scopus citations


We propose a new paradigm for aligning a phoneme sequence of a speech utterance with its acoustical signal counterpart. In contrast to common HMM-based approaches, our method employs a discriminative learning procedure in which the learning phase is tightly coupled with the alignment task at hand. The alignment function we devise is based on mapping the input acoustic-symbolic representations of the speech utterance along with the target alignment into an abstract vector space. We suggest a specific mapping into the abstract vector-space which utilizes standard speech features (e.g. spectral distances) as well as confidence outputs of a framewise phoneme classifier. Building on techniques used for large margin methods for predicting whole sequences, our alignment function distills to a classifier in the abstract vector-space which separates correct alignments from incorrect ones. We describe a simple iterative algorithm for learning the alignment function and discuss its formal properties. Experiments with the TIMIT corpus show that our method outperforms the current state-of-the-art approaches.

Original languageEnglish (US)
Number of pages4
StatePublished - 2005
Event9th European Conference on Speech Communication and Technology - Lisbon, Portugal
Duration: Sep 4 2005Sep 8 2005


Other9th European Conference on Speech Communication and Technology

All Science Journal Classification (ASJC) codes

  • General Engineering


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