Efficient Combination of Likelihood Recycling and Batch Calculation for Fast Acoustic Likelihood Calculation

スポンサーリンク

概要

• 論文の詳細を見る

• This paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an HMM-based speech recognizer. Recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits computer architecture. To accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic backing-off. Conditional fast processing is based on two criteria. The first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. The second reliability criterion and acoustic backing-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. Large vocabulary spontaneous speech recognition experiments using four different CPU machines under two environmental conditions showed that, compared with the baseline recognizer, recycling and batch calculation, our combined acceleration technique further reduced both of the acoustic likelihood calculation time and the total recognition time. We also performed detailed analyses to reveal each techniques acceleration and environmental dependency mechanisms by classifying types of state likelihoods and counting each of them. The analysis results comfirmed the effectiveness of the combined acceleration technique.

• 2011-03-01

著者

• Nakamura Atsushi

  Ntt Communication Science Laboratories Ntt Corporation

• Takahashi Satoshi

  Ntt Cyber Space Laboratories Ntt Corporation

• OGAWA Atsunori

  NTT Communication Science Laboratories, NTT Corporation

• Ogawa Atsunori

  Ntt Communication Science Laboratories Ntt Corporation

関連論文

• Improved Sequential Dependency Analysis Integrating Labeling-Based Sentence Boundary Detection
• Efficient discriminative training of error corrective models using high-WER competitors (Speech) -- (国際ワークショップ"Asian workshop on speech science and technology")
• Efficient discriminative training of error corrective models using high-WER competitors
• Speech Recognition Based on Student's t-Distribution Derived from Total Bayesian Framework(Speech Recognition, Statistical Modeling for Speech Processing)
• Selection of Shared-State Hidden Markov Model Structure Using Bayesian Criterion(the 2003 IEICE Excellent Paper Award)
• Efficient data selection for speech recognition based on prior confidence estimation
• Efficient Combination of Likelihood Recycling and Batch Calculation for Fast Acoustic Likelihood Calculation
• Production-Oriented Models for Speech Recognition(Speech Recognition, Statistical Modeling for Speech Processing)
• Robust Speech Recognition by Model Adaptation and Normalization Using Pre-Observed Noise
• Model Shrinkage for Discriminative Language Models

スポンサーリンク