• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.56-62
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• 2001

In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12％ in a typical noise situation.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.307-310
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• 2007

In this paper, a packet loss concealment (PLC) algorithm for CELP-type speech coders is proposed to improve the quality of decoded speech under a burst packet loss condition. The proposed algorithm is based on the recovery of voiced excitation using an estimate of the voicing probability and the generation of random excitation by permutating the previously decoded excitation. The voicing probability is estimated from the correlation using the previous correctly decoded excitation and pitch. The proposed algorithm is implemented as a PLC algorithm for G.729 and its performance is compared with PLC employed in G.729 by means of perceptual evaluation of speech quality (PESQ) and an A-B preference test under the random and burst packet losses with rates of 3% and 5%. It is shown that the proposed algorithm provides better speech quality than the PLC of G.729, especially under burst pack losses.