Voice activity detection in noise using modulation spectrum of speech: Investigation of speech frequency and modulation frequency ranges

Abstract

Voice activity detection (VAD) in noisy environments is a very important preprocessing scheme in speech communication technology, a field which includes speech recognition, speech coding, speech enhancement and captioning video contents. We have developed a VAD method for noisy environments based on the modulation spectrum. In Experiment 1, we investigate the optimal ranges of speech and modulation frequencies for the proposed algorithm by using the simulated data in the CENSREC-1-C corpus. Results show that when we combine an upper limit frequency between 1,000 and 2,000 Hz with a lower limit frequency of less than 300 Hz as speech frequency bands, error rates are lower than with other bands. Furthermore, when we use the frequency components of the modulation spectrum between 3-9, 3-11, 3-14, 3-18, 4-9, 4-11, 4-14, 4-18, 5-7, 5-9, 5-11, or 5-14 Hz, the proposed method performs VAD well. In Experiment 2, we use one of the best parameter settings from Experiment 1 and evaluate the real environment data in the CENSREC-1-C corpus by comparing our method with other conventional methods. Improvements were observed from the VAD results for each SNR condition and noise type.

Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children

“…Human vocal frequencies are constricted to a specific range, generally 80Hz to 4000Hz are considered vocal frequencies [18]. Thereby audio segments containing no vocal frequencies or very few vocal frequencies for a very short duration could be considered noise only segments.…”

“…Using voice activity detection (VAD) [18], duration of vocal frequencies occurring is identified. The ratio of the vocal to non-vocal frequencies duration (V-NV) is calculated per segment using the identified duration and total duration.…”

Enhancement of speech dynamics for voice activity detection using DNN

“…Periodic to aperiodic component ratios were employed in [9]. Pek et al [10] used modulation indices of the modulation spectra of speech data. Kinnunen and Rajad [11] introduced likelihood ratio-based VAD method in which speech and non-speech models are trained on an utterance-by-utterance basis using mel-frequency cepstral coefficients (MFCCs).…”