Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

Phoneme Alignment Based on Discriminative Learning

Joseph Keshet (1), Shai Shalev-Shwartz (1), Yoram Singer (2), Dan Chazan (3)

(1) Hebrew University, Israel; (2) Google Inc., USA; (3) IBM Haifa Labs, Israel

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 vectorspace 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.

Full Paper

Bibliographic reference.  Keshet, Joseph / Shalev-Shwartz, Shai / Singer, Yoram / Chazan, Dan (2005): "Phoneme alignment based on discriminative learning", In INTERSPEECH-2005, 2961-2964.