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A Large Margin Algorithm for Speech and Audio Segmentation
Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer and Dan Chazan
IEEE Transaction on Audio, Speech and Language Processing
2006.
AbstractWe describe and analyze a discriminative algorithm for learning to segment an
audio signal given a sequence of events that tags the signal. We demonstrate
the applicability of our method through the tasks of speech phoneme segmentation and
music-to-score alignment. In the former task, the events that tag the speech signal
are phonemes and in the latter task, the events are musical notes. Our goal is to learn
a segmentation function whose input is an audio signal along with its accompanying
event sequence and its output is a timing sequence representing the actual start time
of each event in the audio signal. Generalizing the notion of separation with a margin
used in support vector machines (SVM) for binary classification, we cast the learning
task as the problem of finding a direction vector in an abstract vector-space. To do so,
we devise a mapping of the input signal and the event sequence along with any
possible timing sequence into an abstract vector-space. Thus, each possible timing
sequence corresponds to a vector in the vector-space, and the predicted timing
sequence is the one whose projection onto a direction vector in this vector-space is
maximal. We set the direction vector to be the solution of a minimization problem
with a large set of constraints. Each constraint enforces a gap between the projection
of the correct target timing sequence and the projection of an alternative, incorrect,
timing sequence onto the direction vector. Despite the large number of constraints,
we provide a simple iterative algorithm for efficiently learning the direction vector and
analyze the formal properties of the resulting learning algorithm. We experiment
with our learning algorithm in applications of phonetic segmentation and music-to-score
alignment by comparing its performance to the results obtained by a generative
hidden Markov model (HMM) for segmentation. Our experiments indicate that the discriminative
algorithm significantly outperforms the commonly used HMM-based approach. [Edit]
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