In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We also evaluated these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further reported results using the proposed methods to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 59.2%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. All the data used on our experiments have been released to the public as a language identification corpus.

Speaker diarization finds contiguous speaker segments in an audio recording and clusters them by speaker identity, without any a-priori knowledge. Diarization is typically based on short-term spectral features such as Mel-frequency cepstral coefficients (MFCCs). Though these features carry average information about the vocal tract characteristics of a speaker, they are also susceptible to factors unrelated to the speaker identity. In this study, we propose an artificial neural network (ANN) architecture to learn a feature transform that is optimized for speaker diarization. We train a multi-hidden-layer ANN to judge whether two given speech segments came from the same or different speakers, using a shared transform of the input features that feeds into a bottleneck layer. We then use the bottleneck layer activations as features, either alone or in combination with baseline MFCC features in a multistream mode, for speaker diarization on test data. The resulting system is evaluated on various corpora of multi-party meetings. A combination of MFCC and ANN features gives up to 14% relative reduction in diarization error, demonstrating that these features are providing an additional independent source of knowledge.Index Terms-speaker diarization, artificial neural networks, discriminative feature extraction

Abstract-Overlapping speech has been identified as one of the main sources of errors in diarization of meeting room conversations. Therefore, overlap detection has become an important step prior to speaker diarization. Studies on conversational analysis have shown that overlapping speech is more likely to occur at specific parts of a conversation. They have also shown that overlap occurrence is correlated with various conversational features such as speech, silence patterns and speaker turn changes. We use features capturing this higher level information from structure of a conversation such as silence and speaker change statistics to improve acoustic feature based classifier of overlapping and single-speaker speech classes. The silence and speaker change statistics are computed over a long-term window (around 3-4 seconds) and are used to predict the probability of overlap in the window. These estimates are then incorporated into a acoustic feature based classifier as prior probabilities of the classes. Experiments conducted on three corpora (AMI, NIST-RT and ICSI) have shown that the proposed method improves the performance of acoustic featurebased overlap detector on all the corpora. They also reveal that the model based on long-term conversational features used to estimate probability of overlap which is learned from AMI corpus generalizes to meetings from other corpora (NIST-RT and ICSI). Moreover, experiments on ICSI corpus reveal that the proposed method also improves laughter overlap detection. Consequently, applying overlap handling techniques to speaker diarization using the detected overlap results in reduction of diarization error rate (DER) on all the three corpora.

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