  In this work, we present a new Vector Space Model (VSM) of speech utterances
for the task of spoken dialect identification. Generally, DID systems are built
using two sets of features that are extracted from speech utterances; acoustic
and phonetic. The acoustic and phonetic features are used to form vector
representations of speech utterances in an attempt to encode information about
the spoken dialects. The Phonotactic and Acoustic VSMs, thus formed, are used
for the task of DID. The aim of this paper is to construct a single VSM that
encodes information about spoken dialects from both the Phonotactic and
Acoustic VSMs. Given the two views of the data, we make use of a well known
multi-view dimensionality reduction technique known as Canonical Correlation
Analysis (CCA), to form a single vector representation for each speech
utterance that encodes dialect specific discriminative information from both
the phonetic and acoustic representations. We refer to this approach as feature
space combination approach and show that our CCA based feature vector
representation performs better on the Arabic DID task than the phonetic and
acoustic feature representations used alone. We also present the feature space
combination approach as a viable alternative to the model based combination
approach, where two DID systems are built using the two VSMs (Phonotactic and
Acoustic) and the final prediction score is the output score combination from
the two systems.
