2010年10月
Long-Term Spectro-Temporal and Static Harmonic Features for Voice Activity Detection
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
- ,
- ,
- 巻
- 4
- 号
- 5
- 開始ページ
- 834
- 終了ページ
- 844
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1109/JSTSP.2010.2069750
- 出版者・発行元
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Accurate voice activity detection (VAD) is important for robust automatic speech recognition (ASR) systems. This paper proposes a statistical-model-based noise-robust VAD algorithm using long-term temporal information and harmonic-structure-based features in speech. Long-term temporal information has recently become an ASR focus, but has not yet been deeply investigated for VAD. In this paper, we first consider the temporal features in a cepstral domain calculated over the average phoneme duration. In contrast, the harmonic structures are well-known bearers of acoustic information in human voices, but that information is difficult to exploit statistically. This paper further describes a new method to exploit the harmonic structure information with statistical models, providing additional noise robustness. The proposed method including both the long-term temporal and the static harmonic features led to considerable improvements under low SNR conditions, with 77.7% error reduction on average as compared with the ETSI AFE-VAD in our VAD testing. In addition, the word error rate was reduced by 29.1% in a test that included a full ASR system.
- リンク情報
- ID情報
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- DOI : 10.1109/JSTSP.2010.2069750
- ISSN : 1932-4553
- Web of Science ID : WOS:000283266800008