Design and Applications of Embedded Systems for Speech Processing

Wang, Jhing-Fa; Lin, Po‐Chun; Chen, Bo-Wei

doi:10.5772/38558

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2012

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“…In some systems with limited memory resources—for example, in speech engines for embedded systems or multilingual speech engines used in spoken-oriented human-robot dialogue systems [4, 5]—using large pronunciation lexicons is not feasible [6]. In addition, the search time in such lexicons may be long if the lexicons are too large to be stored in the main memory or cache.…”

Section: Introductionmentioning

confidence: 99%

FST-Based Pronunciation Lexicon Compression for Speech Engines

Golob

Gros

Žganec

et al. 2012

International Journal of Advanced Robotic Systems

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Finite‐state transducers are frequently used for pronunciation lexicon representations in speech engines, in which memory and processing resources are scarce. This paper proposes two possibilities for further reducing the memory footprint of finite‐state transducers representing pronunciation lexicons. First, different alignments of grapheme and allophone transcriptions are studied and a reduction in the number of states of up to 30% is reported. Second, a combination of grapheme‐to‐allophone rules with a finite‐state transducer is proposed, which yields a 65% smaller finite‐state transducer than conventional approaches

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Section: Introductionmentioning

confidence: 99%