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In this paper, we investigate semi-supervised training for low resource languages where the initial systems may have high error rate (≥ 70.0% word eror rate). To handle the lack of data, we study semi-supervised techniques including data selection, data weighting, discriminative training and multilayer perceptron learning to improve system performance. The entire suite of semi-supervised methods presented in this paper was evaluated under the IARPA Babel program for the keyword spotting tasks. Our semi-supervised system had the best performance in the OpenKWS13 surprise language evaluation for the limited condition. In this paper, we describe our work on the Turkish and Vietnamese systems. [ABSTRACT FROM PUBLISHER]

We present two techniques that are shown to yield improved Keyword Spotting (KWS) performance when using the ATWV/MTWV performance measures: (i) score normalization, where the scores of different keywords become commensurate with each other and they more closely correspond to the probability of being correct than raw posteriors; and (ii) system combination, where the detections of multiple systems are merged together, and their scores are interpolated with weights which are optimized using MTWV as the maximization criterion. Both score normalization and system combination approaches show that significant gains in ATWV/MTWV can be obtained, sometimes on the order of 8–10 points (absolute), in five different languages. A variant of these methods resulted in the highest performance for the official surprise language evaluation for the IARPA-funded Babel project in April 2013. [ABSTRACT FROM PUBLISHER]

Text-to-speech synthesis (TTS) is the final stage in the speech-tospeech (S2S) translation pipeline, producing an audible rendition of translated text in the target language. TTS systems typically rely on a lexicon to look up pronunciations for each word in the input text. This is problematic when the target language is dialectal Arabic, because the statistical machine translation (SMT) system usually produces undiacritized text output. Many words in the latter possess multiple pronunciations; the correct choice must be inferred from context. In this paper, we present a weakly supervised pronunciation prediction approach for undiacritized dialectal Arabic in S2S systems that leverages automatic speech recognition (ASR) to obtain parallel training data for pronunciation prediction. Additionally, we show that incorporating source language features derived from SMT-generated automatic word alignment further improves automatic pronunciation prediction accuracy. [ABSTRACT FROM PUBLISHER]

Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams. [ABSTRACT FROM PUBLISHER]