کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
566056 875918 2006 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hard C-means clustering for voice activity detection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Hard C-means clustering for voice activity detection
چکیده انگلیسی

An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The proposed speech/pause discrimination method is based on a hard-decision clustering approach built on a set of subband log-energies and noise prototypes that define a cluster. Detecting the presence of speech (a new cluster) is achieved using a basic sequential algorithm scheme (BSAS) according to a given “distance” (in this case, geometrical distance) and a suitable threshold. The accuracy of the Cluster VAD (ClVAD) algorithm lies in the use of a decision function defined over a multiple-observation (MO) window of averaged subband log-energies and a suitable noise subspace model defined in terms of prototypes. In addition, the reduced computational cost of the clustering approach makes it adequate for real-time applications, i.e. speech recognition. An exhaustive analysis is conducted on the Spanish SpeechDat-Car databases in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs such as ITU-T G.729, ETSI GSM AMR and ETSI AFE and a representative set of recently reported VAD algorithms for noise robust speech processing.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Speech Communication - Volume 48, Issue 12, December 2006, Pages 1638–1649
نویسندگان
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