Finally language independent and unsupervised acoustic models are trained for languages with no training data. Data augmentation via using multilingual bottleneck features is offered (the topic is also covered in ). Deep neural network acoustic models are used both as feature extractor for a GMM-based HMM system and to compute state posteriors and convert them into scaled likelihoods by normalizing by the state priors.
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Reference provides an extensive description of modern methods used to build a keyword spotting system for 10 low-resource languages with primary focus on Assamese, Bengali, Haitian Creole, Lao, and Zulu. For example, describes an approach for keyword spotting in Cantonese based on large vocabulary speech recognition and shows positive results of applying neural networks to recognition lattice rescoring. Thanks to the IARPA Babel program aimed at building systems that can be rapidly applied to any human language in order to provide effective search capability for analysts to efficiently process massive amounts of real-world recorded speech in recent years wide research has been held to develop technologies for spoken term detection systems for low-resource languages. Deep neural networks application in LVCSR is starting to achieve wide adoption. Most recently a number of innovative approaches to spoken term detection were offered such as various recognition system combination and score normalization, reporting 20% increase in spoken term detection quality (measured as ATWV). While each of the approaches has got its pros and cons the latter starts to be prominent due to public availability of baseline algorithms, cheaper hardware to run intensive calculations required in LVCSR and, most importantly, high-quality results. Modern keyword spotting engines usually rely on either of three approaches, namely, phonetic lattice search, word-based models, and large vocabulary speech recognition. Keyword spotting technology makes a substantial part of such systems. The need to understand business trends, ensure public security, and improve the quality of customer service has caused a sustainable development of speech analytics systems which transform speech data into a measurable and searchable index of words, phrases, and paralinguistic markers. The effective combination of baseline statistic methods, real-world training data, and the intensive use of linguistic knowledge led to a quality result applicable to industrial use.
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The system is based on CMU Sphinx open-source speech recognition platform and on the linguistic models and algorithms developed by Speech Drive LLC. The description of system architecture and the user interface is provided. Key algorithms and system settings are described, including the pronunciation variation algorithm, and the experimental results on the real-life telecom data are provided. The paper describes the key concepts of a word spotting system for Russian based on large vocabulary continuous speech recognition.