Applying the Bi-level HMM for Robust Voice-activity Detection
نویسندگان
چکیده
منابع مشابه
Applying the Bi-level HMM for Robust Voice-activity Detection
This paper presents a voice-activity detection (VAD) method for sound sequences with various SNRs. For real-time VAD applications, it is inadequate to employ a post-processing for the removal of burst clippings from the VAD output decision. To tackle this problem, building on the bilevel hidden Markov model, for which a state layer is inserted into a typical hidden Markov model (HMM), we formul...
متن کاملA hybrid HMM/traps model for robust voice activity detection
We present three voice activity detection (VAD) algorithms that are suitable for the off-line processing of noisy speech and compare their performance on SPINE-2 evaluation data using speech recognition error rate as the quality metric. One VAD system is a simple HMM-based segmenter that uses normalized log-energy and a degree of voicing measure as raw features. The other two VAD systems focus ...
متن کاملRobust visual speakingness detection using bi-level HMM
Visual voice activity detection (V-VAD) plays an important role in both HCI and HRI, affecting both the conversation strategy and sync between humans and robots/computers. The typical speakingness decision of V-VAD consists of post-processing for signal smoothing and classification using thresholding. Several parameters, ensuring a good trade-off between hit rate and false alarm, are usually he...
متن کاملNoise Robust Voice Activity Detection
Voice activity detection (VAD) is a fundamental task in various speech-related applications, such as speech coding, speaker diarization and speech recognition. It is often defined as the problem of distinguishing speech from silence/noise. A typical VAD system consists of two core parts: a feature extraction and a speech/ non-speech decision mechanism. The first part extracts a set of parameter...
متن کاملDuration-embedded bi-HMM for expressive voice conversion
This paper presents a duration-embedded Bi-HMM framework for expressive voice conversion. First, Ward’s minimum variance clustering method is used to cluster all the conversion units (sub-syllables) in order to reduce the number of conversion models as well as the size of the required training database. The duration-embedded Bi-HMM trained with the EM algorithm is built for each sub-syllable cl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Electrical Engineering and Technology
سال: 2017
ISSN: 1975-0102
DOI: 10.5370/jeet.2017.12.1.373