Long-Term Spectro-Temporal and Static Harmonic Features for Voice Activity Detection

Abstract

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.