We propose a neural network model to estimate word translation probabilities for Cross-Lingual Information Retrieval (CLIR). The model estimates better probabilities for word translations than automatic word alignments alone, and generalizes to unseen source-target word pairs. We further improve the lexical neural translation model (and subsequently CLIR), by incorporating source word context, and by encoding the character sequences of input source words to generate translations of out-of-vocabulary words. To be effective, neural network models typically need training on large amounts of data labeled directly on the final task, in this case relevance to queries. In contrast, our approach only requires parallel data to train the translation model, and uses an unsupervised model to compute CLIR relevance scores.

In this paper we describe the 2016 BBN conversational telephone speech keyword spotting system; the culmination of four years of research and development under the IARPA Babel program. The system was constructed in response to the NIST Open Keyword Search (OpenKWS) evaluation of 2016. We present our technological breakthroughs in building top-performing keyword spotting processing systems for new languages, in the face of limited transcribed speech, noisy conditions, and limited system build time of one week.

With the completion of the IARPA Babel program, it is possible to systematically analyze the performance of speech recognition systems across a wide variety of languages. We select 16 languages from the dataset and compare performance using a deep neural network-based acoustic model. The focus is on keyword spotting using the actual term-weighted value (ATWV) metric. We demonstrate that ATWV is keyword dependent, and that this must be accounted for in any cross-language analysis. Further, we show that while performance across languages does not track with any particular feature of the language, it is correlated with inter-annotator agreement.

In this paper we describe the Vietnamese conversational telephone speech keyword spotting system under the IARPA Babel program for the 2013 evaluation conducted by NIST. The system contains several, recently developed, novel methods that significantly improve speech-to-text and keyword spotting performance such as stacked bottleneck neural network features, white listing, score normalization, and improvements on semi-supervised training methods. These methods resulted in the highest performance for the official IARPA Babel surprise language evaluation of 2013.

This paper presents a set of techniques that we used to improve our keyword search system for the third phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded radio communication channels. The results for both Levantine and Farsi, which are the two target languages for the keyword search (KWS) task, are reported. About 13% absolute reduction in word error rate (from 70.2% to 57.6%) is achieved by using acoustic features derived from stacked Multi-Layer Perceptrons (MLP) and Deep Neural Network (DNN) acoustic models. In addition to score normalization and score/system combination for keyword search, we showed that the false alarm rate at the target false reject rate (15%) was reduced by about 1% (from 5.39% to 4.45%) by reducing the deletion errors of the speech-to-text system. Index Terms: speech recognition, KWS, MLP, DNN

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.

The AMI(DA) system is a meeting room speech recognition system that has been developed and evaluated in the context of the NIST Rich Text (RT) evaluations. Recently, the "Distant Access" requirements of the AMIDA project have necessitated that the system operate in real-time. Another more difficult requirement is that the system fit into a live meeting transcription scenario. We describe an infrastructure that has allowed the AMI(DA) system to evolve into one that fulfils these extra requirements. We emphasise the components that address the live and real-time aspects.