Acoustic Modeling with Deep Belief Networks for Russian Speech Recognition

Zulkarneev, Mikhail; Grigoryan, Ruben; Shamraev, Nikolay

doi:10.1007/978-3-319-01931-4_3

Cited by 4 publications

(1 citation statement)

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“…It is noteworthy however that most frequently the research is conducted to meet a more general target of creating LVCSR systems per se with no specific focus on spoken term detection. The most wellknown systems include Yandex SpeechKit [19] used to recognize spoken search queries via web and mobile applications, real-time speech recognition system by Speech Technology Center [20] used for transcribing speech in the broadcasting news, LVCSR system developed by SPIIRAS [21,22] used for recognizing speech in multimodal environments, and speech recognition system by scientific institute Specvuzavtomatika [23] based on deep neural networks. Current paper presents the results of the ongoing research underlying the commercial software for speech analytics.…”

Section: Introductionmentioning

confidence: 99%

A Russian Keyword Spotting System Based on Large Vocabulary Continuous Speech Recognition and Linguistic Knowledge

Smirnov¹,

Ignatov²,

Gusev³

et al. 2016

Journal of Electrical and Computer Engineering

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The paper describes the key concepts of a word spotting system for Russian based on large vocabulary continuous speech recognition. 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 description of system architecture and the user interface is provided. 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 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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Section: Introductionmentioning

confidence: 99%